This notebook is from databricks document: - https://docs.databricks.com/_static/notebooks/package-cells.html
As you know, we need to eventually package and deploy our models, say using sbt.
However it is nice to be in a notebook environment to prototype and build intuition and create better pipelines.
Using package cells we can be in the best of both worlds to an extent.
Package Cells
Package cells are special cells that get compiled when executed. These cells have no visibility with respect to the rest of the notebook. You may think of them as separate scala files.
This means that only class and object definitions may go inside this cell. You may not have any variable or function definitions lying around by itself. The following cell will not work.
If you wish to use custom classes and/or objects defined within notebooks reliably in Spark, and across notebook sessions, you must use package cells to define those classes.
Unless you use package cells to define classes, you may also come across obscure bugs as follows:
// We define a class
case class TestKey(id: Long, str: String)
defined class TestKey
// we use that class as a key in the group by
val rdd = sc.parallelize(Array((TestKey(1L, "abd"), "dss"), (TestKey(2L, "ggs"), "dse"), (TestKey(1L, "abd"), "qrf")))
rdd.groupByKey().collect
rdd: org.apache.spark.rdd.RDD[(TestKey, String)] = ParallelCollectionRDD[0] at parallelize at command-2971213210276613:2
res0: Array[(TestKey, Iterable[String])] = Array((TestKey(1,abd),CompactBuffer(dss)), (TestKey(1,abd),CompactBuffer(qrf)), (TestKey(2,ggs),CompactBuffer(dse)))
What went wrong above? Even though we have two elements for the key TestKey(1L, "abd"), they behaved as two different keys resulting in:
Array[(TestKey, Iterable[String])] = Array(
(TestKey(2,ggs),CompactBuffer(dse)),
(TestKey(1,abd),CompactBuffer(dss)),
(TestKey(1,abd),CompactBuffer(qrf)))
Once we define our case class within a package cell, we will not face this issue.
package com.databricks.example
case class TestKey(id: Long, str: String)
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import com.databricks.example
val rdd = sc.parallelize(Array(
(example.TestKey(1L, "abd"), "dss"), (example.TestKey(2L, "ggs"), "dse"), (example.TestKey(1L, "abd"), "qrf")))
rdd.groupByKey().collect
import com.databricks.example
rdd: org.apache.spark.rdd.RDD[(com.databricks.example.TestKey, String)] = ParallelCollectionRDD[2] at parallelize at command-2971213210276616:3
res2: Array[(com.databricks.example.TestKey, Iterable[String])] = Array((TestKey(1,abd),CompactBuffer(dss, qrf)), (TestKey(2,ggs),CompactBuffer(dse)))
As you can see above, the group by worked above, grouping two elements (dss, qrf) under TestKey(1,abd).
These cells behave as individual source files, therefore only classes and objects can be defined inside these cells.
package x.y.z
val aNumber = 5 // won't work
def functionThatWillNotWork(a: Int): Int = a + 1
The following cell is the way to go.
package x.y.z
object Utils {
val aNumber = 5 // works!
def functionThatWillWork(a: Int): Int = a + 1
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.z.Utils
Utils.functionThatWillWork(Utils.aNumber)
import x.y.z.Utils
res5: Int = 6
Why did we get the warning: classes defined within packages cannot be redefined without a cluster restart?
Classes that get compiled with the package cells get dynamically injected into Spark's classloader. Currently it's not possible to remove classes from Spark's classloader. Any classes that you define and compile will have precedence in the classloader, therefore once you recompile, it will not be visible to your application.
Well that kind of beats the purpose of iterative notebook development if I have to restart the cluster, right? In that case, you may just rename the package to x.y.z2 during development/fast iteration and fix it once everything works.
One thing to remember with package cells is that it has no visiblity regarding the notebook environment.
- The SparkContext will not be defined as
sc. - The SQLContext will not be defined as
sqlContext. - Did you import a package in a separate cell? Those imports will not be available in the package cell and have to be remade.
- Variables imported through
%runcells will not be available.
It is really a standalone file that just looks like a cell in a notebook. This means that any function that uses anything that was defined in a separate cell, needs to take that variable as a parameter or the class needs to take it inside the constructor.
package x.y.zpackage
import org.apache.spark.SparkContext
case class IntArray(values: Array[Int])
class MyClass(sc: SparkContext) {
def sparkSum(array: IntArray): Int = {
sc.parallelize(array.values).reduce(_ + _)
}
}
object MyClass {
def sparkSum(sc: SparkContext, array: IntArray): Int = {
sc.parallelize(array.values).reduce(_ + _)
}
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.zpackage._
val array = IntArray(Array(1, 2, 3, 4, 5))
val myClass = new MyClass(sc)
myClass.sparkSum(array)
import x.y.zpackage._
array: x.y.zpackage.IntArray = IntArray([I@44a91426)
myClass: x.y.zpackage.MyClass = x.y.zpackage.MyClass@5d3b142
res7: Int = 15
MyClass.sparkSum(sc, array)
res8: Int = 15
Build Packages Locally
Although package cells are quite handy for quick prototyping in notebook environemnts, it is better to develop packages locally on your laptop and then upload the packaged or assembled jar file into databricks to use the classes and methods developed in the package.
Core ideas in Monte Carlo simulation
- modular arithmetic gives pseudo-random streams that are indistiguishable from 'true' Uniformly distributed samples in integers from \({0,1,2,...,m}\)
- by diving the integer streams from above by \(m\) we get samples from \({0/m,1/m,...,(m-1)/m}\) and "pretend" this to be samples from the Uniform(0,1) RV
- we can use inverse distribution function of von Neumann's rejection sampler to convert samples from Uniform(0,1) RV to the following:
- any other random variable
- vector of random variables that could be dependent
- or more generally other random structures:
- random graphs and networks
- random walks or (sensible perturbations of live traffic data on open street maps for hypothesis tests)
- models of interacting paticle systems in ecology / chemcal physics, etc...
def frameIt( u:String, h:Int ) : String = {
"""<iframe
src=""""+ u+""""
width="95%" height="""" + h + """"
sandbox>
<p>
<a href="http://spark.apache.org/docs/latest/index.html">
Fallback link for browsers that, unlikely, don't support frames
</a>
</p>
</iframe>"""
}
displayHTML(frameIt("https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)",500))
- https://en.wikipedia.org/wiki/Inversetransformsampling
- https://en.wikipedia.org/wiki/Rejection_sampling - will revisit below for Expoential RV
breeze.stats.distributions
Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.
import breeze.stats.distributions._
val poi = new Poisson(3.0);
import breeze.stats.distributions._
poi: breeze.stats.distributions.Poisson = Poisson(3.0)
val s = poi.sample(5); // let's draw five samples - black-box
s: IndexedSeq[Int] = Vector(2, 4, 3, 4, 6)
Getting probabilities of the Poisson samples
s.map( x => poi.probabilityOf(x) ) // PMF
res0: IndexedSeq[Double] = Vector(0.22404180765538775, 0.16803135574154085, 0.22404180765538775, 0.16803135574154085, 0.05040940672246224)
val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = MappedRand(Poisson(3.0),$Lambda$5909/1062631843@70cdbd4c)
breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
res1: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(3.0660000000000034,3.250894894894892,1000)
(poi.mean, poi.variance) // population mean and variance
res2: (Double, Double) = (3.0,3.0)
Exponential random Variable
Let's focus on getting our hands direty with a common random variable.
NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.
val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
expo.rate // what is the rate parameter
res3: Double = 0.5
A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.
expo.probability(0, math.log(2) * expo.rate)
res4: Double = 0.5
expo.probability(math.log(2) * expo.rate, 10000.0)
res5: Double = 0.5
expo.probability(0.0, 1.5)
res6: Double = 0.950212931632136
The above result means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well.
1 - math.exp(-3.0) // the CDF of the Exponential RV with rate parameter 3
res7: Double = 0.950212931632136
val samples = expo.sample(2).sorted; // built-in black box - we will roll our own shortly in Spark
samples: IndexedSeq[Double] = Vector(2.412493444974671, 3.6802759985818687)
expo.probability(samples(0), samples(1));
res8: Double = 0.007390811722912227
breeze.stats.meanAndVariance(expo.samples.take(10000)); // mean and variance of the sample
res9: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(2.0109026469746554,4.073504140726905,10000)
(1 / expo.rate, 1 / (expo.rate * expo.rate)) // mean and variance of the population
res10: (Double, Double) = (2.0,4.0)
import spark.implicits._
import org.apache.spark.sql.functions._
import spark.implicits._
import org.apache.spark.sql.functions._
val df = spark.range(1000).toDF("Id") // just make a DF of 1000 row indices
df: org.apache.spark.sql.DataFrame = [Id: bigint]
df.show(5)
+---+
| Id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id| rand|
+---+--------------------+
| 0|0.024042816995877625|
| 1| 0.4763832311243641|
| 2| 0.3392911406256911|
| 3| 0.6282132043405342|
| 4| 0.9665960987271114|
+---+--------------------+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id| rand|
+---+--------------------+
| 0|0.024042816995877625|
| 1| 0.4763832311243641|
| 2| 0.3392911406256911|
| 3| 0.6282132043405342|
| 4| 0.9665960987271114|
+---+--------------------+
only showing top 5 rows
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
val dfRand = df.select($"Id", rand(seed=879664) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+-------------------+
| Id| rand|
+---+-------------------+
| 0| 0.269224348263137|
| 1|0.20433965628039852|
| 2| 0.8481228617934538|
| 3| 0.6665103087537884|
| 4| 0.7712898780552342|
+---+-------------------+
only showing top 5 rows
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
Let's use the inverse CDF of the Exponential RV to transform these samples from the Uniform(0,1) RV into those from the Exponential RV.
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
.withColumn("one",lit(1.0))
.withColumn("rate",lit(0.5))
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 2 more fields]
dfRand.show(5)
+---+--------------------+---+----+
| Id| rand|one|rate|
+---+--------------------+---+----+
| 0|0.024042816995877625|1.0| 0.5|
| 1| 0.4763832311243641|1.0| 0.5|
| 2| 0.3392911406256911|1.0| 0.5|
| 3| 0.6282132043405342|1.0| 0.5|
| 4| 0.9665960987271114|1.0| 0.5|
+---+--------------------+---+----+
only showing top 5 rows
val dfExpRand = dfRand.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand")) // samples from expo(rate=0.5)
dfExpRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
dfExpRand.show(5)
+---+--------------------+---+----+--------------------+
| Id| rand|one|rate| expo_sample|
+---+--------------------+---+----+--------------------+
| 0|0.024042816995877625|1.0| 0.5|0.048673126808362034|
| 1| 0.4763832311243641|1.0| 0.5| 1.2939904386814756|
| 2| 0.3392911406256911|1.0| 0.5| 0.8288839819270684|
| 3| 0.6282132043405342|1.0| 0.5| 1.9788694379168241|
| 4| 0.9665960987271114|1.0| 0.5| 6.798165162486205|
+---+--------------------+---+----+--------------------+
only showing top 5 rows
display(dfExpRand)
| Id | rand | one | rate | expo_sample |
|---|---|---|---|---|
| 0.0 | 2.4042816995877625e-2 | 1.0 | 0.5 | 4.8673126808362034e-2 |
| 1.0 | 0.4763832311243641 | 1.0 | 0.5 | 1.2939904386814756 |
| 2.0 | 0.3392911406256911 | 1.0 | 0.5 | 0.8288839819270684 |
| 3.0 | 0.6282132043405342 | 1.0 | 0.5 | 1.9788694379168241 |
| 4.0 | 0.9665960987271114 | 1.0 | 0.5 | 6.798165162486205 |
| 5.0 | 0.8269758822163711 | 1.0 | 0.5 | 3.508648570093974 |
| 6.0 | 0.3404052214826948 | 1.0 | 0.5 | 0.8322592089262 |
| 7.0 | 0.5658253891225227 | 1.0 | 0.5 | 1.6686169931673005 |
| 8.0 | 0.11737981749647353 | 1.0 | 0.5 | 0.24972063061386013 |
| 9.0 | 0.7848668745585088 | 1.0 | 0.5 | 3.072996508744745 |
| 10.0 | 0.10665116151243736 | 1.0 | 0.5 | 0.2255562755600773 |
| 11.0 | 0.34971775733163646 | 1.0 | 0.5 | 0.8606975816973311 |
| 12.0 | 3.349566781262969e-2 | 1.0 | 0.5 | 6.813899599567436e-2 |
| 13.0 | 0.334063669089834 | 1.0 | 0.5 | 0.8131224244912618 |
| 14.0 | 0.4105052702588069 | 1.0 | 0.5 | 1.0569789985263547 |
| 15.0 | 0.22519777483698333 | 1.0 | 0.5 | 0.5102949510683874 |
| 16.0 | 0.21124107883891907 | 1.0 | 0.5 | 0.47458910937069004 |
| 17.0 | 0.12332274767843698 | 1.0 | 0.5 | 0.26323273531964136 |
| 18.0 | 4.324422155449681e-2 | 1.0 | 0.5 | 8.841423006745963e-2 |
| 19.0 | 3.933267172708754e-2 | 1.0 | 0.5 | 8.025420479220831e-2 |
| 20.0 | 0.8162723223794007 | 1.0 | 0.5 | 3.388601261211285 |
| 21.0 | 7.785566240785313e-2 | 1.0 | 0.5 | 0.16210703862675058 |
| 22.0 | 0.1244015150504949 | 1.0 | 0.5 | 0.26569528726942954 |
| 23.0 | 0.5313795676534989 | 1.0 | 0.5 | 1.5159243018004147 |
| 24.0 | 0.8177063965639969 | 1.0 | 0.5 | 3.4042733720631824 |
| 25.0 | 0.7757379079578416 | 1.0 | 0.5 | 2.98987971469377 |
| 26.0 | 0.9019086568714294 | 1.0 | 0.5 | 4.643712323362237 |
| 27.0 | 0.5793202308042869 | 1.0 | 0.5 | 1.7317667559523853 |
| 28.0 | 4.913530139386124e-2 | 1.0 | 0.5 | 0.10076699863506142 |
| 29.0 | 0.6984668419742928 | 1.0 | 0.5 | 2.3977505839877837 |
| 30.0 | 1.579141234703818e-2 | 1.0 | 0.5 | 3.18348501444197e-2 |
| 31.0 | 0.3170147319677016 | 1.0 | 0.5 | 0.762563978285606 |
| 32.0 | 0.6243818511105477 | 1.0 | 0.5 | 1.9583644261768265 |
| 33.0 | 0.9720896733059052 | 1.0 | 0.5 | 7.157517052464175 |
| 34.0 | 6.406755887221727e-2 | 1.0 | 0.5 | 0.13242396678361196 |
| 35.0 | 0.8275324400058236 | 1.0 | 0.5 | 3.515092236401242 |
| 36.0 | 0.4223760255739688 | 1.0 | 0.5 | 1.0976643705892708 |
| 37.0 | 0.6237792697270514 | 1.0 | 0.5 | 1.9551585184791915 |
| 38.0 | 0.30676209726152626 | 1.0 | 0.5 | 0.7327640894184466 |
| 39.0 | 0.7209798363387625 | 1.0 | 0.5 | 2.5529424571699533 |
| 40.0 | 0.5408086036735253 | 1.0 | 0.5 | 1.556576340750279 |
| 41.0 | 0.22254408000797787 | 1.0 | 0.5 | 0.5034566621599345 |
| 42.0 | 0.6478051949133747 | 1.0 | 0.5 | 2.0871416658496442 |
| 43.0 | 0.6186563924196967 | 1.0 | 0.5 | 1.9281089062362353 |
| 44.0 | 0.2689179760895458 | 1.0 | 0.5 | 0.6264592354306907 |
| 45.0 | 0.1310898372764776 | 1.0 | 0.5 | 0.28103107825164747 |
| 46.0 | 0.8459785027719904 | 1.0 | 0.5 | 3.741326187841892 |
| 47.0 | 0.7844461581951896 | 1.0 | 0.5 | 3.069089109365284 |
| 48.0 | 0.3195541476806115 | 1.0 | 0.5 | 0.7700140609818293 |
| 49.0 | 0.326892158146161 | 1.0 | 0.5 | 0.7916994433570004 |
| 50.0 | 0.4392312327086285 | 1.0 | 0.5 | 1.1568932758900772 |
| 51.0 | 0.5377790694946909 | 1.0 | 0.5 | 1.543424595293625 |
| 52.0 | 0.4369640582174875 | 1.0 | 0.5 | 1.1488236262486446 |
| 53.0 | 0.26672242876690055 | 1.0 | 0.5 | 0.6204619408451196 |
| 54.0 | 0.9743181848790359 | 1.0 | 0.5 | 7.323944240755433 |
| 55.0 | 0.5636877255447679 | 1.0 | 0.5 | 1.6587941323713102 |
| 56.0 | 0.8214194684040824 | 1.0 | 0.5 | 3.44543124420649 |
| 57.0 | 4.2394218638494574e-2 | 1.0 | 0.5 | 8.663817482333575e-2 |
| 58.0 | 0.2986030735782996 | 1.0 | 0.5 | 0.7093626466953578 |
| 59.0 | 0.9251464009715654 | 1.0 | 0.5 | 5.184442172120483 |
| 60.0 | 0.9768531076059933 | 1.0 | 0.5 | 7.531789490600966 |
| 61.0 | 0.4147651110232632 | 1.0 | 0.5 | 1.0714839854387161 |
| 62.0 | 0.4525161010109643 | 1.0 | 0.5 | 1.2048444519262322 |
| 63.0 | 7.638776464132979e-2 | 1.0 | 0.5 | 0.1589259082490955 |
| 64.0 | 0.8203900440907219 | 1.0 | 0.5 | 3.433935381714252 |
| 65.0 | 0.9746746584977457 | 1.0 | 0.5 | 7.35189948830076 |
| 66.0 | 0.41832172801299305 | 1.0 | 0.5 | 1.083675562745256 |
| 67.0 | 3.3131200470495226e-2 | 1.0 | 0.5 | 6.738494114620364e-2 |
| 68.0 | 0.10008247055930286 | 1.0 | 0.5 | 0.21090430762250917 |
| 69.0 | 0.4268021831375646 | 1.0 | 0.5 | 1.113048783438383 |
| 70.0 | 0.8213380254753332 | 1.0 | 0.5 | 3.444519337826204 |
| 71.0 | 0.47431121070064797 | 1.0 | 0.5 | 1.2860917933318652 |
| 72.0 | 0.68679164992865 | 1.0 | 0.5 | 2.321773309424188 |
| 73.0 | 0.7057803676609208 | 1.0 | 0.5 | 2.4468574835462356 |
| 74.0 | 0.32184620342731496 | 1.0 | 0.5 | 0.776762356325894 |
| 75.0 | 0.3589329148022541 | 1.0 | 0.5 | 0.8892423408857425 |
| 76.0 | 0.7448782338623747 | 1.0 | 0.5 | 2.7320286670643386 |
| 77.0 | 0.9740613004640858 | 1.0 | 0.5 | 7.304038469785621 |
| 78.0 | 0.7453909609063684 | 1.0 | 0.5 | 2.736052180743762 |
| 79.0 | 6.853610233988816e-2 | 1.0 | 0.5 | 0.14199569380051374 |
| 80.0 | 0.336012781812836 | 1.0 | 0.5 | 0.8189847588182159 |
| 81.0 | 0.2963058480260412 | 1.0 | 0.5 | 0.7028229208813994 |
| 82.0 | 0.38627232525200883 | 1.0 | 0.5 | 0.9764079513820425 |
| 83.0 | 0.9097248909817963 | 1.0 | 0.5 | 4.809787008391862 |
| 84.0 | 0.20679745942261907 | 1.0 | 0.5 | 0.46335335878970363 |
| 85.0 | 0.16582497569469312 | 1.0 | 0.5 | 0.36262407472768277 |
| 86.0 | 0.6960738838737711 | 1.0 | 0.5 | 2.381941292346302 |
| 87.0 | 0.890094879396298 | 1.0 | 0.5 | 4.416275650715409 |
| 88.0 | 0.8258738496535992 | 1.0 | 0.5 | 3.4959504809275685 |
| 89.0 | 0.4066244237870885 | 1.0 | 0.5 | 1.0438554620679272 |
| 90.0 | 0.44414816275414526 | 1.0 | 0.5 | 1.1745070000344822 |
| 91.0 | 0.8820223175378497 | 1.0 | 0.5 | 4.274519608161631 |
| 92.0 | 0.3814200182827251 | 1.0 | 0.5 | 0.9606575597598919 |
| 93.0 | 0.491252177347836 | 1.0 | 0.5 | 1.3516056440673538 |
| 94.0 | 0.9042145561359308 | 1.0 | 0.5 | 4.691289097027222 |
| 95.0 | 0.8293982814391448 | 1.0 | 0.5 | 3.536847140695551 |
| 96.0 | 0.6957817964411346 | 1.0 | 0.5 | 2.38002012037681 |
| 97.0 | 0.46108317902841456 | 1.0 | 0.5 | 1.2363880819986401 |
| 98.0 | 0.658234439306857 | 1.0 | 0.5 | 2.14726054405596 |
| 99.0 | 0.43175037735005384 | 1.0 | 0.5 | 1.130388960612226 |
| 100.0 | 0.7719881477151886 | 1.0 | 0.5 | 2.9567153353469786 |
| 101.0 | 6.397418621476536e-2 | 1.0 | 0.5 | 0.13222444810891013 |
| 102.0 | 0.44410081132020296 | 1.0 | 0.5 | 1.1743366329905425 |
| 103.0 | 0.9364231799229901 | 1.0 | 0.5 | 5.511012678534471 |
| 104.0 | 0.5453471566845608 | 1.0 | 0.5 | 1.576442265948382 |
| 105.0 | 0.7346238968987211 | 1.0 | 0.5 | 2.653214404406468 |
| 106.0 | 0.3493365535175731 | 1.0 | 0.5 | 0.8595254994862054 |
| 107.0 | 0.5230037795263933 | 1.0 | 0.5 | 1.480493423321206 |
| 108.0 | 0.6886814284788837 | 1.0 | 0.5 | 2.333877090719847 |
| 109.0 | 0.7731252946239237 | 1.0 | 0.5 | 2.966714745163421 |
| 110.0 | 2.276168830750991e-2 | 1.0 | 0.5 | 4.604946957472576e-2 |
| 111.0 | 0.264951110753621 | 1.0 | 0.5 | 0.6156365319998442 |
| 112.0 | 0.14616140102686104 | 1.0 | 0.5 | 0.3160261944637388 |
| 113.0 | 3.066619523108849e-2 | 1.0 | 0.5 | 6.229248529326732e-2 |
| 114.0 | 0.49017052188807597 | 1.0 | 0.5 | 1.3473579316333943 |
| 115.0 | 9.835381148010536e-2 | 1.0 | 0.5 | 0.20706617613153588 |
| 116.0 | 8.828156810764243e-2 | 1.0 | 0.5 | 0.1848481470740138 |
| 117.0 | 0.6868446103815881 | 1.0 | 0.5 | 2.3221115183574854 |
| 118.0 | 0.4067862578001439 | 1.0 | 0.5 | 1.0444010055396156 |
| 119.0 | 0.44070042328022496 | 1.0 | 0.5 | 1.1621400679168603 |
| 120.0 | 0.19523663294276805 | 1.0 | 0.5 | 0.4344139974853618 |
| 121.0 | 0.34930320794183467 | 1.0 | 0.5 | 0.8594230049585315 |
| 122.0 | 0.37713232784782635 | 1.0 | 0.5 | 0.9468423740115679 |
| 123.0 | 0.6429635193876022 | 1.0 | 0.5 | 2.0598346316676848 |
| 124.0 | 0.5726782020473655 | 1.0 | 0.5 | 1.7004358488091253 |
| 125.0 | 0.3013872989669776 | 1.0 | 0.5 | 0.7173175321607891 |
| 126.0 | 0.3469657736553403 | 1.0 | 0.5 | 0.8522514741503977 |
| 127.0 | 0.3062597218624077 | 1.0 | 0.5 | 0.7313152550300903 |
| 128.0 | 0.875187814442521 | 1.0 | 0.5 | 4.161890374256847 |
| 129.0 | 0.4331447487608374 | 1.0 | 0.5 | 1.1353025933318837 |
| 130.0 | 0.40669672646934574 | 1.0 | 0.5 | 1.0440991764725864 |
| 131.0 | 0.10105613827873039 | 1.0 | 0.5 | 0.21306938341833667 |
| 132.0 | 0.7598058191143243 | 1.0 | 0.5 | 2.852615191501923 |
| 133.0 | 0.7882552857444408 | 1.0 | 0.5 | 3.104747815909758 |
| 134.0 | 0.41196255767852374 | 1.0 | 0.5 | 1.0619293113867083 |
| 135.0 | 0.23956793737837967 | 1.0 | 0.5 | 0.5477370075782519 |
| 136.0 | 0.301117813053034 | 1.0 | 0.5 | 0.716546192187808 |
| 137.0 | 0.4359866364410945 | 1.0 | 0.5 | 1.1453546670228079 |
| 138.0 | 0.9682651399507317 | 1.0 | 0.5 | 6.90067903242789 |
| 139.0 | 0.10678335714273168 | 1.0 | 0.5 | 0.22585225268919112 |
| 140.0 | 0.2693226978676214 | 1.0 | 0.5 | 0.6275667277003328 |
| 141.0 | 0.5668302775070247 | 1.0 | 0.5 | 1.6732513179468083 |
| 142.0 | 0.7096151948853401 | 1.0 | 0.5 | 2.473096642771549 |
| 143.0 | 0.7574375459472653 | 1.0 | 0.5 | 2.8329921185549365 |
| 144.0 | 0.49769652658104957 | 1.0 | 0.5 | 1.3771016264425564 |
| 145.0 | 0.45884572648841415 | 1.0 | 0.5 | 1.2281017543594028 |
| 146.0 | 0.3288386747446713 | 1.0 | 0.5 | 0.7974914915764556 |
| 147.0 | 0.5410136911709724 | 1.0 | 0.5 | 1.557469795251325 |
| 148.0 | 0.6336220553800798 | 1.0 | 0.5 | 2.008179685617141 |
| 149.0 | 0.375647544465191 | 1.0 | 0.5 | 0.9420804749655175 |
| 150.0 | 0.10142418148506294 | 1.0 | 0.5 | 0.21388838577034947 |
| 151.0 | 0.4721728293756773 | 1.0 | 0.5 | 1.2779727544450452 |
| 152.0 | 0.7518331384524283 | 1.0 | 0.5 | 2.787307860488268 |
| 153.0 | 6.676079335730689e-2 | 1.0 | 0.5 | 0.13818745319668635 |
| 154.0 | 0.8053497028664404 | 1.0 | 0.5 | 3.2731013568502365 |
| 155.0 | 0.6436250826649516 | 1.0 | 0.5 | 2.063543927419815 |
| 156.0 | 0.28432165233043427 | 1.0 | 0.5 | 0.6690488961123614 |
| 157.0 | 0.33131329522029473 | 1.0 | 0.5 | 0.8048792646192418 |
| 158.0 | 0.4390246975576103 | 1.0 | 0.5 | 1.1561567971907922 |
| 159.0 | 0.9576167619712271 | 1.0 | 0.5 | 6.322004648835221 |
| 160.0 | 0.2191897706358752 | 1.0 | 0.5 | 0.4948462856734829 |
| 161.0 | 1.1385964425825512e-2 | 1.0 | 0.5 | 2.2902561570486413e-2 |
| 162.0 | 0.679111841181116 | 1.0 | 0.5 | 2.2733252629142697 |
| 163.0 | 0.3189329784940914 | 1.0 | 0.5 | 0.768189122733912 |
| 164.0 | 0.5494024842158357 | 1.0 | 0.5 | 1.5943615282559738 |
| 165.0 | 0.4995339525325778 | 1.0 | 0.5 | 1.3844310395116766 |
| 166.0 | 0.6133797107105055 | 1.0 | 0.5 | 1.900624464472158 |
| 167.0 | 0.18020899363866738 | 1.0 | 0.5 | 0.3974116829996968 |
| 168.0 | 0.3549472573452819 | 1.0 | 0.5 | 0.8768463879435472 |
| 169.0 | 0.6992620601216817 | 1.0 | 0.5 | 2.403032050173198 |
| 170.0 | 0.24085612119427668 | 1.0 | 0.5 | 0.5511279118150403 |
| 171.0 | 0.1609790935708849 | 1.0 | 0.5 | 0.3510393091093769 |
| 172.0 | 0.9711366383834527 | 1.0 | 0.5 | 7.090364504579701 |
| 173.0 | 0.38455734034855193 | 1.0 | 0.5 | 0.9708269965922631 |
| 174.0 | 0.7140149247000265 | 1.0 | 0.5 | 2.503631307579515 |
| 175.0 | 0.6231707003505403 | 1.0 | 0.5 | 1.9519259602967969 |
| 176.0 | 0.6056033420465197 | 1.0 | 0.5 | 1.8607962600766825 |
| 177.0 | 0.9191984318132235 | 1.0 | 0.5 | 5.03151781078248 |
| 178.0 | 0.31549310135748787 | 1.0 | 0.5 | 0.7581131118739614 |
| 179.0 | 0.4450415722231543 | 1.0 | 0.5 | 1.1777241458955992 |
| 180.0 | 0.4583485174404076 | 1.0 | 0.5 | 1.2262650109539877 |
| 181.0 | 0.8948048795370758 | 1.0 | 0.5 | 4.503876726373103 |
| 182.0 | 0.5068846132533013 | 1.0 | 0.5 | 1.4140241642575553 |
| 183.0 | 0.474099338611806 | 1.0 | 0.5 | 1.2852858815130643 |
| 184.0 | 3.968996187382612e-2 | 1.0 | 0.5 | 8.099818055602134e-2 |
| 185.0 | 0.3812923011104965 | 1.0 | 0.5 | 0.9602446657347301 |
| 186.0 | 0.4490455490374048 | 1.0 | 0.5 | 1.1922062787516934 |
| 187.0 | 0.18885603597795153 | 1.0 | 0.5 | 0.4186194528265905 |
| 188.0 | 0.14835262533228888 | 1.0 | 0.5 | 0.32116543464514347 |
| 189.0 | 1.0239721160210324e-2 | 1.0 | 0.5 | 2.0585015521643695e-2 |
| 190.0 | 0.7812863611722196 | 1.0 | 0.5 | 3.0399839801249593 |
| 191.0 | 0.40315501297127665 | 1.0 | 0.5 | 1.032195705046974 |
| 192.0 | 0.27623054102229216 | 1.0 | 0.5 | 0.6465647282627558 |
| 193.0 | 0.3476188563715197 | 1.0 | 0.5 | 0.8542526234724834 |
| 194.0 | 0.40005917714351136 | 1.0 | 0.5 | 1.0218485144052543 |
| 195.0 | 0.690531564629077 | 1.0 | 0.5 | 2.3457983558717395 |
| 196.0 | 0.6243976540207664 | 1.0 | 0.5 | 1.9584485714328679 |
| 197.0 | 0.6936108435709636 | 1.0 | 0.5 | 2.365798463973219 |
| 198.0 | 0.5580754032112615 | 1.0 | 0.5 | 1.6332320139161962 |
| 199.0 | 0.5077201201493545 | 1.0 | 0.5 | 1.4174157254902722 |
| 200.0 | 0.24217287516689856 | 1.0 | 0.5 | 0.5545999737072358 |
| 201.0 | 0.5866156019146013 | 1.0 | 0.5 | 1.7667547458541368 |
| 202.0 | 0.13480908234777678 | 1.0 | 0.5 | 0.289610164710414 |
| 203.0 | 0.8592340571612042 | 1.0 | 0.5 | 3.921313495527709 |
| 204.0 | 0.5015919278028333 | 1.0 | 0.5 | 1.3926722308356132 |
| 205.0 | 0.9524596166650006 | 1.0 | 0.5 | 6.092351507324665 |
| 206.0 | 0.7897861372493181 | 1.0 | 0.5 | 3.1192597448504493 |
| 207.0 | 0.992502482003315 | 1.0 | 0.5 | 9.786366493971752 |
| 208.0 | 0.7190312712917515 | 1.0 | 0.5 | 2.539023803153757 |
| 209.0 | 4.905914294075553e-2 | 1.0 | 0.5 | 0.10060681726863402 |
| 210.0 | 7.292405751548614e-2 | 1.0 | 0.5 | 0.15143958783801956 |
| 211.0 | 0.7448249635342986 | 1.0 | 0.5 | 2.731611103575813 |
| 212.0 | 7.024810923770186e-2 | 1.0 | 0.5 | 0.14567502510922456 |
| 213.0 | 0.6499895525785409 | 1.0 | 0.5 | 2.0995845503371515 |
| 214.0 | 0.2347304081263114 | 1.0 | 0.5 | 0.5350541991171558 |
| 215.0 | 0.5554391604460018 | 1.0 | 0.5 | 1.62133672301354 |
| 216.0 | 0.45655849903065526 | 1.0 | 0.5 | 1.2196664242497346 |
| 217.0 | 0.7734426024060143 | 1.0 | 0.5 | 2.969513910302441 |
| 218.0 | 0.13396935067038884 | 1.0 | 0.5 | 0.28766995842079884 |
| 219.0 | 0.44732092861928796 | 1.0 | 0.5 | 1.1859555740131458 |
| 220.0 | 0.532360601482588 | 1.0 | 0.5 | 1.5201155921058849 |
| 221.0 | 0.8353842261129955 | 1.0 | 0.5 | 3.6082823273928657 |
| 222.0 | 0.4415319449291001 | 1.0 | 0.5 | 1.1651157196663489 |
| 223.0 | 7.107670903097285e-2 | 1.0 | 0.5 | 0.14745823038641734 |
| 224.0 | 0.2587810776672368 | 1.0 | 0.5 | 0.5989185111514972 |
| 225.0 | 0.8783986898097359 | 1.0 | 0.5 | 4.214015069834256 |
| 226.0 | 0.8772054215086009 | 1.0 | 0.5 | 4.194484826673072 |
| 227.0 | 0.8792794678306639 | 1.0 | 0.5 | 4.228554112475122 |
| 228.0 | 0.9704733519907857 | 1.0 | 0.5 | 7.04492420208042 |
| 229.0 | 0.5911949898493493 | 1.0 | 0.5 | 1.7890339688352386 |
| 230.0 | 0.1881415659970821 | 1.0 | 0.5 | 0.4168585927615873 |
| 231.0 | 0.27280409460696464 | 1.0 | 0.5 | 0.6371187335647226 |
| 232.0 | 0.8997405595900834 | 1.0 | 0.5 | 4.599988097103155 |
| 233.0 | 0.805703299381296 | 1.0 | 0.5 | 3.2767378072078768 |
| 234.0 | 0.12469160466325713 | 1.0 | 0.5 | 0.26635800581530467 |
| 235.0 | 4.5007500538482015e-2 | 1.0 | 0.5 | 9.210358499849247e-2 |
| 236.0 | 0.5406049480217902 | 1.0 | 0.5 | 1.5556895188057671 |
| 237.0 | 0.7089850749112344 | 1.0 | 0.5 | 2.4687614483220965 |
| 238.0 | 0.4050350886413143 | 1.0 | 0.5 | 1.038505695363729 |
| 239.0 | 0.18681323647475456 | 1.0 | 0.5 | 0.4135889487659769 |
| 240.0 | 0.23557258393827696 | 1.0 | 0.5 | 0.5372564022794455 |
| 241.0 | 0.32077303736600393 | 1.0 | 0.5 | 0.7735998942749642 |
| 242.0 | 0.27136665743377386 | 1.0 | 0.5 | 0.6331692658855377 |
| 243.0 | 8.600287997937284e-2 | 1.0 | 0.5 | 0.1798557169901321 |
| 244.0 | 0.7600332087019175 | 1.0 | 0.5 | 2.8545094696108473 |
| 245.0 | 0.41783858737032487 | 1.0 | 0.5 | 1.0820150568291664 |
| 246.0 | 0.8293413803867148 | 1.0 | 0.5 | 3.5361801888515507 |
| 247.0 | 0.7333405374926266 | 1.0 | 0.5 | 2.6435657118891944 |
| 248.0 | 0.8772160342203914 | 1.0 | 0.5 | 4.194657687243259 |
| 249.0 | 5.301719603632349e-2 | 1.0 | 0.5 | 0.10894868878842578 |
| 250.0 | 0.9026197915984311 | 1.0 | 0.5 | 4.658264576287863 |
| 251.0 | 0.23914978744357662 | 1.0 | 0.5 | 0.5466375404979962 |
| 252.0 | 0.4968410398019236 | 1.0 | 0.5 | 1.3736982691137887 |
| 253.0 | 0.5384099353933083 | 1.0 | 0.5 | 1.5461561756710942 |
| 254.0 | 0.8907377100572391 | 1.0 | 0.5 | 4.428007915156274 |
| 255.0 | 0.9066080833593035 | 1.0 | 0.5 | 4.741900966190897 |
| 256.0 | 0.6829340520390256 | 1.0 | 0.5 | 2.297290978019848 |
| 257.0 | 0.6571961620790489 | 1.0 | 0.5 | 2.1411937930382208 |
| 258.0 | 0.3669337202760652 | 1.0 | 0.5 | 0.9143603100291875 |
| 259.0 | 0.5097789017273943 | 1.0 | 0.5 | 1.4257975373650602 |
| 260.0 | 0.19359708351354943 | 1.0 | 0.5 | 0.4303435299938143 |
| 261.0 | 0.4198903553742862 | 1.0 | 0.5 | 1.0890763016996372 |
| 262.0 | 0.8275841161941834 | 1.0 | 0.5 | 3.515691583104315 |
| 263.0 | 0.5245014108343904 | 1.0 | 0.5 | 1.486782728111085 |
| 264.0 | 0.20212413144380958 | 1.0 | 0.5 | 0.45160449363942295 |
| 265.0 | 0.6588746438800428 | 1.0 | 0.5 | 2.1510105119936225 |
| 266.0 | 0.6090021282726809 | 1.0 | 0.5 | 1.8781063243284435 |
| 267.0 | 0.8237249377890233 | 1.0 | 0.5 | 3.4714193009141487 |
| 268.0 | 0.39616901908028135 | 1.0 | 0.5 | 1.0089219062444619 |
| 269.0 | 0.46974976524528655 | 1.0 | 0.5 | 1.268812485625708 |
| 270.0 | 0.1430991547459871 | 1.0 | 0.5 | 0.30886613379677225 |
| 271.0 | 0.8900411063855386 | 1.0 | 0.5 | 4.415297354889731 |
| 272.0 | 0.8908538606807918 | 1.0 | 0.5 | 4.430135134053363 |
| 273.0 | 0.7571966906626759 | 1.0 | 0.5 | 2.8310071799990957 |
| 274.0 | 0.45373865486682496 | 1.0 | 0.5 | 1.2093155273309903 |
| 275.0 | 0.5105750301805768 | 1.0 | 0.5 | 1.429048215969818 |
| 276.0 | 0.8703161512938904 | 1.0 | 0.5 | 4.085311447017532 |
| 277.0 | 0.6263910497078812 | 1.0 | 0.5 | 1.9690912320594496 |
| 278.0 | 0.8879229935650624 | 1.0 | 0.5 | 4.377138172983162 |
| 279.0 | 0.4592765782031528 | 1.0 | 0.5 | 1.2296947319737286 |
| 280.0 | 0.3332564208219061 | 1.0 | 0.5 | 0.8106994919909759 |
| 281.0 | 0.7769330816977306 | 1.0 | 0.5 | 3.000566940929362 |
| 282.0 | 0.47688504943059673 | 1.0 | 0.5 | 1.2959080966079761 |
| 283.0 | 0.5519970113210297 | 1.0 | 0.5 | 1.6059107508619763 |
| 284.0 | 0.6116679748262741 | 1.0 | 0.5 | 1.8917891406149387 |
| 285.0 | 0.23344992857646873 | 1.0 | 0.5 | 0.5317105161007432 |
| 286.0 | 0.547367711693101 | 1.0 | 0.5 | 1.5853504174370916 |
| 287.0 | 0.45229676089796134 | 1.0 | 0.5 | 1.2040433464044202 |
| 288.0 | 0.2232542399926749 | 1.0 | 0.5 | 0.5052843787124497 |
| 289.0 | 4.812719615823391e-2 | 1.0 | 0.5 | 9.864772505442727e-2 |
| 290.0 | 0.9924754840764293 | 1.0 | 0.5 | 9.779177599019986 |
| 291.0 | 0.23112957413067814 | 1.0 | 0.5 | 0.525665641185302 |
| 292.0 | 0.7136348699646448 | 1.0 | 0.5 | 2.500975207955907 |
| 293.0 | 0.7097521146725535 | 1.0 | 0.5 | 2.474039888248458 |
| 294.0 | 0.2534528100999812 | 1.0 | 0.5 | 0.584592898262904 |
| 295.0 | 0.14159162922836588 | 1.0 | 0.5 | 0.30535067223059725 |
| 296.0 | 0.286451779155965 | 1.0 | 0.5 | 0.6750105216507689 |
| 297.0 | 0.9194163349443866 | 1.0 | 0.5 | 5.0369186336261 |
| 298.0 | 0.8565261729771216 | 1.0 | 0.5 | 3.8832053010040535 |
| 299.0 | 0.9963446427954837 | 1.0 | 0.5 | 11.223122920363782 |
| 300.0 | 0.44312606197302895 | 1.0 | 0.5 | 1.1708327755623118 |
| 301.0 | 0.3333780003343948 | 1.0 | 0.5 | 0.8110642217087809 |
| 302.0 | 8.100442373331274e-2 | 1.0 | 0.5 | 0.16894794055205103 |
| 303.0 | 0.9114279295358697 | 1.0 | 0.5 | 4.847877405697818 |
| 304.0 | 0.1420851500734669 | 1.0 | 0.5 | 0.30650085385781334 |
| 305.0 | 0.3998938086649432 | 1.0 | 0.5 | 1.0212973077353178 |
| 306.0 | 0.43509561875462044 | 1.0 | 0.5 | 1.1421975977833791 |
| 307.0 | 0.2607501670030833 | 1.0 | 0.5 | 0.6042386923167598 |
| 308.0 | 0.7861629300585208 | 1.0 | 0.5 | 3.0850818187043214 |
| 309.0 | 0.4238639225351366 | 1.0 | 0.5 | 1.1028228011782435 |
| 310.0 | 0.8296205577621936 | 1.0 | 0.5 | 3.5394546320186806 |
| 311.0 | 0.797111503088613 | 1.0 | 0.5 | 3.190197454292849 |
| 312.0 | 4.847906767173793e-2 | 1.0 | 0.5 | 9.93871863877833e-2 |
| 313.0 | 0.2272307227002427 | 1.0 | 0.5 | 0.5155495038419591 |
| 314.0 | 0.28798275880438495 | 1.0 | 0.5 | 0.6793063054019258 |
| 315.0 | 0.9119630135350312 | 1.0 | 0.5 | 4.859996504134525 |
| 316.0 | 0.9247506337150422 | 1.0 | 0.5 | 5.173895593701935 |
| 317.0 | 0.313064117705499 | 1.0 | 0.5 | 0.7510286422175019 |
| 318.0 | 0.10267561230063371 | 1.0 | 0.5 | 0.2166756921335689 |
| 319.0 | 0.7800056454356652 | 1.0 | 0.5 | 3.0283067880604637 |
| 320.0 | 0.9880505590690435 | 1.0 | 0.5 | 8.854141571438799 |
| 321.0 | 0.5199897620784429 | 1.0 | 0.5 | 1.4678956926088331 |
| 322.0 | 0.9631286266437556 | 1.0 | 0.5 | 6.6006396376399366 |
| 323.0 | 0.6526508389996869 | 1.0 | 0.5 | 2.1148495545527686 |
| 324.0 | 0.44331946385558707 | 1.0 | 0.5 | 1.1715274946406817 |
| 325.0 | 0.7657175467075138 | 1.0 | 0.5 | 2.9024556523792184 |
| 326.0 | 0.8587810198761535 | 1.0 | 0.5 | 3.914887085632034 |
| 327.0 | 0.17717500338441594 | 1.0 | 0.5 | 0.39002348344727783 |
| 328.0 | 0.6400146315652561 | 1.0 | 0.5 | 2.043383783189525 |
| 329.0 | 0.5429950331288752 | 1.0 | 0.5 | 1.5661220394400488 |
| 330.0 | 0.1519419896098827 | 1.0 | 0.5 | 0.3296124741021507 |
| 331.0 | 0.2317505808062127 | 1.0 | 0.5 | 0.5272816679676412 |
| 332.0 | 0.9587236527128663 | 1.0 | 0.5 | 6.374931295970532 |
| 333.0 | 0.3957876337227971 | 1.0 | 0.5 | 1.0076590860947048 |
| 334.0 | 0.7520930392631228 | 1.0 | 0.5 | 2.7894035230513716 |
| 335.0 | 0.5219702075466132 | 1.0 | 0.5 | 1.4761644422492128 |
| 336.0 | 0.2718748554726389 | 1.0 | 0.5 | 0.6345646874711566 |
| 337.0 | 0.43339022569610974 | 1.0 | 0.5 | 1.1361688818668743 |
| 338.0 | 0.654264591604089 | 1.0 | 0.5 | 2.124163024174615 |
| 339.0 | 0.21929573754620202 | 1.0 | 0.5 | 0.4951177321724933 |
| 340.0 | 0.6463525037251117 | 1.0 | 0.5 | 2.0789092703893584 |
| 341.0 | 0.40363690799123597 | 1.0 | 0.5 | 1.0338111652659434 |
| 342.0 | 0.5464987531537895 | 1.0 | 0.5 | 1.5815145200909522 |
| 343.0 | 0.2794928368714429 | 1.0 | 0.5 | 0.6555998434128101 |
| 344.0 | 0.9679988160115437 | 1.0 | 0.5 | 6.883964754455241 |
| 345.0 | 0.9374750976583118 | 1.0 | 0.5 | 5.5443807282558 |
| 346.0 | 0.5043382898350185 | 1.0 | 0.5 | 1.403723241814482 |
| 347.0 | 0.37940066231801983 | 1.0 | 0.5 | 0.9541391883810038 |
| 348.0 | 0.16327499528946765 | 1.0 | 0.5 | 0.35651962241911506 |
| 349.0 | 0.6814882638496493 | 1.0 | 0.5 | 2.2881919129060675 |
| 350.0 | 0.7593963453274154 | 1.0 | 0.5 | 2.8492085714581132 |
| 351.0 | 8.2716537875273e-2 | 1.0 | 0.5 | 0.17267747098244784 |
| 352.0 | 0.9864630812678177 | 1.0 | 0.5 | 8.604669210362268 |
| 353.0 | 0.10362777838201431 | 1.0 | 0.5 | 0.2187990527194605 |
| 354.0 | 0.3018065167087475 | 1.0 | 0.5 | 0.7185180358786611 |
| 355.0 | 0.1981827289480086 | 1.0 | 0.5 | 0.4417490773130338 |
| 356.0 | 0.2519352721846343 | 1.0 | 0.5 | 0.5805315404780675 |
| 357.0 | 0.37720491978724313 | 1.0 | 0.5 | 0.947075477038406 |
| 358.0 | 3.334532342207808e-2 | 1.0 | 0.5 | 6.782791058537331e-2 |
| 359.0 | 0.49142274606532255 | 1.0 | 0.5 | 1.3522762997820723 |
| 360.0 | 0.7658697744126436 | 1.0 | 0.5 | 2.9037555976399063 |
| 361.0 | 0.430584327777621 | 1.0 | 0.5 | 1.1262891608345273 |
| 362.0 | 0.18774246417057594 | 1.0 | 0.5 | 0.41587565350859623 |
| 363.0 | 0.43213770208564395 | 1.0 | 0.5 | 1.131752645804099 |
| 364.0 | 0.5254311249185892 | 1.0 | 0.5 | 1.4906970370036838 |
| 365.0 | 0.21632961088151903 | 1.0 | 0.5 | 0.48753353815321687 |
| 366.0 | 0.5866314734106438 | 1.0 | 0.5 | 1.766831535403086 |
| 367.0 | 0.2127018826329221 | 1.0 | 0.5 | 0.47829660009371444 |
| 368.0 | 0.762880957964817 | 1.0 | 0.5 | 2.8783859537652954 |
| 369.0 | 0.7092177159493649 | 1.0 | 0.5 | 2.4703609132000657 |
| 370.0 | 0.4668447454243504 | 1.0 | 0.5 | 1.25788522569854 |
| 371.0 | 8.984189541128584e-2 | 1.0 | 0.5 | 0.18827390651246967 |
| 372.0 | 0.8401841637115331 | 1.0 | 0.5 | 3.6674662997626886 |
| 373.0 | 0.1769541258232804 | 1.0 | 0.5 | 0.3894866793361383 |
| 374.0 | 0.32759234424821526 | 1.0 | 0.5 | 0.793780983603854 |
| 375.0 | 0.7549436630804378 | 1.0 | 0.5 | 2.812534296521244 |
| 376.0 | 0.5406091390043264 | 1.0 | 0.5 | 1.5557077645471398 |
| 377.0 | 7.226167164454322e-2 | 1.0 | 0.5 | 0.15001111942581233 |
| 378.0 | 3.9174989741246e-2 | 1.0 | 0.5 | 7.992595578900699e-2 |
| 379.0 | 0.43530038855865494 | 1.0 | 0.5 | 1.1429227007667369 |
| 380.0 | 0.19425051459560916 | 1.0 | 0.5 | 0.4319647938819565 |
| 381.0 | 0.2690512431721227 | 1.0 | 0.5 | 0.6268238435762967 |
| 382.0 | 0.6122531354155542 | 1.0 | 0.5 | 1.894805126272543 |
| 383.0 | 0.8144904991435312 | 1.0 | 0.5 | 3.3692983613822673 |
| 384.0 | 0.44435200950498954 | 1.0 | 0.5 | 1.1752405917023965 |
| 385.0 | 0.40334065577595324 | 1.0 | 0.5 | 1.0328178822819034 |
| 386.0 | 0.6712465016160815 | 1.0 | 0.5 | 2.2248941081495746 |
| 387.0 | 0.610470870145168 | 1.0 | 0.5 | 1.8856332573010395 |
| 388.0 | 0.3753417849616416 | 1.0 | 0.5 | 0.941101269529212 |
| 389.0 | 0.5760552709957159 | 1.0 | 0.5 | 1.7163043767384967 |
| 390.0 | 0.8958499208801377 | 1.0 | 0.5 | 4.523844703206438 |
| 391.0 | 0.6375933030298327 | 1.0 | 0.5 | 2.0299764509716054 |
| 392.0 | 0.4996734547364764 | 1.0 | 0.5 | 1.3849886064074164 |
| 393.0 | 0.43886175915693537 | 1.0 | 0.5 | 1.1555759704008648 |
| 394.0 | 2.1262190388847357e-2 | 1.0 | 0.5 | 4.298297362707392e-2 |
| 395.0 | 0.2612612193574123 | 1.0 | 0.5 | 0.6056217946491596 |
| 396.0 | 0.9053544127651678 | 1.0 | 0.5 | 4.715232048676575 |
| 397.0 | 5.715293130681975e-3 | 1.0 | 0.5 | 1.146337583128797e-2 |
| 398.0 | 0.2947471489241186 | 1.0 | 0.5 | 0.6983977729250015 |
| 399.0 | 8.460986179878727e-2 | 1.0 | 0.5 | 0.17680984806639513 |
| 400.0 | 0.30069220409765574 | 1.0 | 0.5 | 0.7153285923659113 |
| 401.0 | 0.997746334354857 | 1.0 | 0.5 | 12.190394425627364 |
| 402.0 | 0.18308241899251243 | 1.0 | 0.5 | 0.40443413850132504 |
| 403.0 | 0.3105228234689785 | 1.0 | 0.5 | 0.7436433675489958 |
| 404.0 | 0.9179013483983409 | 1.0 | 0.5 | 4.999667373023489 |
| 405.0 | 0.11926274283498617 | 1.0 | 0.5 | 0.2539918600578725 |
| 406.0 | 0.43776630003690786 | 1.0 | 0.5 | 1.1516753585822221 |
| 407.0 | 0.4762249664302428 | 1.0 | 0.5 | 1.2933860241922412 |
| 408.0 | 0.8482098508688078 | 1.0 | 0.5 | 3.770512619720418 |
| 409.0 | 0.4567392533274949 | 1.0 | 0.5 | 1.2203317557106708 |
| 410.0 | 0.4435608409662263 | 1.0 | 0.5 | 1.1723948842501484 |
| 411.0 | 0.50408147116895 | 1.0 | 0.5 | 1.4026872442755594 |
| 412.0 | 0.36104698819880265 | 1.0 | 0.5 | 0.8958487225315878 |
| 413.0 | 0.4194713065185254 | 1.0 | 0.5 | 1.0876321003142069 |
| 414.0 | 7.486133824518293e-2 | 1.0 | 0.5 | 0.1556232962089628 |
| 415.0 | 3.373382017468085e-2 | 1.0 | 0.5 | 6.863186850542395e-2 |
| 416.0 | 0.4932147269781292 | 1.0 | 0.5 | 1.3593357794290557 |
| 417.0 | 0.8616586394474591 | 1.0 | 0.5 | 3.956062042024912 |
| 418.0 | 0.4117184281160471 | 1.0 | 0.5 | 1.0610991639078917 |
| 419.0 | 9.116459643129304e-2 | 1.0 | 0.5 | 0.19118255076481538 |
| 420.0 | 0.5890322019489482 | 1.0 | 0.5 | 1.7784808355923856 |
| 421.0 | 0.43778596825837024 | 1.0 | 0.5 | 1.1517453243828897 |
| 422.0 | 0.5541973559190224 | 1.0 | 0.5 | 1.6157578539111792 |
| 423.0 | 0.5236632829365838 | 1.0 | 0.5 | 1.4832605720870944 |
| 424.0 | 0.6204546477584022 | 1.0 | 0.5 | 1.9375623680773675 |
| 425.0 | 0.970316394356556 | 1.0 | 0.5 | 7.034320768236008 |
| 426.0 | 0.43993687089356626 | 1.0 | 0.5 | 1.1594115421186992 |
| 427.0 | 0.8405879195529187 | 1.0 | 0.5 | 3.6725254570044403 |
| 428.0 | 0.749280638128116 | 1.0 | 0.5 | 2.766842091120008 |
| 429.0 | 0.5488427514547121 | 1.0 | 0.5 | 1.5918786677038552 |
| 430.0 | 0.13747140435906435 | 1.0 | 0.5 | 0.29577395251982774 |
| 431.0 | 0.4576343120245375 | 1.0 | 0.5 | 1.2236296079783713 |
| 432.0 | 0.8148665411500688 | 1.0 | 0.5 | 3.3733566295966297 |
| 433.0 | 0.19547202902357552 | 1.0 | 0.5 | 0.4349990900088173 |
| 434.0 | 0.4890095869756548 | 1.0 | 0.5 | 1.3428089003184602 |
| 435.0 | 0.12715136653541503 | 1.0 | 0.5 | 0.27198624962848195 |
| 436.0 | 0.2032311331013058 | 1.0 | 0.5 | 0.45438129228481317 |
| 437.0 | 0.8787527473448604 | 1.0 | 0.5 | 4.21984681587113 |
| 438.0 | 0.4663010675042861 | 1.0 | 0.5 | 1.2558467916792808 |
| 439.0 | 0.3142004342512348 | 1.0 | 0.5 | 0.7543397443148356 |
| 440.0 | 0.5376680285543044 | 1.0 | 0.5 | 1.5429441860463613 |
| 441.0 | 0.8288823435429962 | 1.0 | 0.5 | 3.530807819241641 |
| 442.0 | 0.5381362365823195 | 1.0 | 0.5 | 1.5449706315293206 |
| 443.0 | 0.8741054855972965 | 1.0 | 0.5 | 4.144621819895548 |
| 444.0 | 0.9650736097727945 | 1.0 | 0.5 | 6.709025137110639 |
| 445.0 | 8.401495565683514e-2 | 1.0 | 0.5 | 0.17551048315515766 |
| 446.0 | 0.16014272925069362 | 1.0 | 0.5 | 0.34904663471346736 |
| 447.0 | 0.6761260538348454 | 1.0 | 0.5 | 2.254801787874353 |
| 448.0 | 0.30491746315485757 | 1.0 | 0.5 | 0.7274493648359186 |
| 449.0 | 0.8823536465498564 | 1.0 | 0.5 | 4.280144318376048 |
| 450.0 | 0.35138375129442345 | 1.0 | 0.5 | 0.8658280669121068 |
| 451.0 | 0.8162562408450885 | 1.0 | 0.5 | 3.388426210497536 |
| 452.0 | 0.673617698352461 | 1.0 | 0.5 | 2.2393717605117374 |
| 453.0 | 8.256976107323988e-2 | 1.0 | 0.5 | 0.1723574716228483 |
| 454.0 | 0.8365768515057421 | 1.0 | 0.5 | 3.6228248778772634 |
| 455.0 | 0.48971308441437955 | 1.0 | 0.5 | 1.3455642637453662 |
| 456.0 | 8.774182190296687e-2 | 1.0 | 0.5 | 0.18366447790309529 |
| 457.0 | 0.24099238723353444 | 1.0 | 0.5 | 0.5514869432829278 |
| 458.0 | 0.5907862348164701 | 1.0 | 0.5 | 1.787035212429029 |
| 459.0 | 0.9282830732565702 | 1.0 | 0.5 | 5.270056963848408 |
| 460.0 | 0.32874248016649343 | 1.0 | 0.5 | 0.7972048609806637 |
| 461.0 | 0.7759823021398251 | 1.0 | 0.5 | 2.9920604438874734 |
| 462.0 | 0.9906880008613337 | 1.0 | 0.5 | 9.352902960980467 |
| 463.0 | 0.35519401347986057 | 1.0 | 0.5 | 0.8776116070547805 |
| 464.0 | 0.22036578800170115 | 1.0 | 0.5 | 0.4978608565413814 |
| 465.0 | 0.8023870524533302 | 1.0 | 0.5 | 3.242889943586732 |
| 466.0 | 0.6555700243159146 | 1.0 | 0.5 | 2.131728945611721 |
| 467.0 | 0.6966117625572236 | 1.0 | 0.5 | 2.385483963919468 |
| 468.0 | 0.31199672354331665 | 1.0 | 0.5 | 0.7479233575368842 |
| 469.0 | 0.10874684309305482 | 1.0 | 0.5 | 0.23025353029665035 |
| 470.0 | 0.7768279542152332 | 1.0 | 0.5 | 2.999624598436637 |
| 471.0 | 0.5194659966765868 | 1.0 | 0.5 | 1.465714573067321 |
| 472.0 | 3.387321287945699e-2 | 1.0 | 0.5 | 6.892040754981558e-2 |
| 473.0 | 0.9137616097231142 | 1.0 | 0.5 | 4.901279675232361 |
| 474.0 | 0.9310800753319032 | 1.0 | 0.5 | 5.349619920714008 |
| 475.0 | 0.8956753753595035 | 1.0 | 0.5 | 4.520495700986587 |
| 476.0 | 0.9734569283996768 | 1.0 | 0.5 | 7.257973044045264 |
| 477.0 | 0.3100378260015375 | 1.0 | 0.5 | 0.7422370063712049 |
| 478.0 | 0.9004067891568347 | 1.0 | 0.5 | 4.613322561880238 |
| 479.0 | 0.3595142946826074 | 1.0 | 0.5 | 0.8910569518000615 |
| 480.0 | 0.5858555389814262 | 1.0 | 0.5 | 1.7630808527274318 |
| 481.0 | 0.7207027332090898 | 1.0 | 0.5 | 2.5509571840094765 |
| 482.0 | 0.5207773901843563 | 1.0 | 0.5 | 1.4711801017479904 |
| 483.0 | 0.5010641338355079 | 1.0 | 0.5 | 1.3905554324221692 |
| 484.0 | 0.7386177898402442 | 1.0 | 0.5 | 2.683543072229971 |
| 485.0 | 0.7514865091276058 | 1.0 | 0.5 | 2.784516291388268 |
| 486.0 | 0.996791280204512 | 1.0 | 0.5 | 11.48376647798819 |
| 487.0 | 0.31973896223026976 | 1.0 | 0.5 | 0.7705573508031445 |
| 488.0 | 0.5409498257000886 | 1.0 | 0.5 | 1.557191525390985 |
| 489.0 | 0.2351915468213719 | 1.0 | 0.5 | 0.5362597290194078 |
| 490.0 | 0.5722823786456511 | 1.0 | 0.5 | 1.6985841286612482 |
| 491.0 | 0.8872393295593034 | 1.0 | 0.5 | 4.364975334044577 |
| 492.0 | 0.7466377111677412 | 1.0 | 0.5 | 2.745869685758634 |
| 493.0 | 0.8944045014489479 | 1.0 | 0.5 | 4.496279071952017 |
| 494.0 | 0.7761279146229962 | 1.0 | 0.5 | 2.993360875321733 |
| 495.0 | 6.282087728304298e-4 | 1.0 | 0.5 | 1.2568123572811955e-3 |
| 496.0 | 2.1755806290161828e-2 | 1.0 | 0.5 | 4.399190658802096e-2 |
| 497.0 | 0.25822043228050306 | 1.0 | 0.5 | 0.5974063169929631 |
| 498.0 | 0.36706789505445514 | 1.0 | 0.5 | 0.9147842435207847 |
| 499.0 | 0.8535706465084604 | 1.0 | 0.5 | 3.8424243911227456 |
| 500.0 | 0.9583607379261688 | 1.0 | 0.5 | 6.357423513924509 |
| 501.0 | 0.1736332737734787 | 1.0 | 0.5 | 0.38143325101594394 |
| 502.0 | 0.7191057722209122 | 1.0 | 0.5 | 2.539554188214649 |
| 503.0 | 0.5866772207662699 | 1.0 | 0.5 | 1.7670528869782172 |
| 504.0 | 0.44342504955089734 | 1.0 | 0.5 | 1.171906870972165 |
| 505.0 | 0.8638825079861409 | 1.0 | 0.5 | 3.988473708673089 |
| 506.0 | 0.19206484030065873 | 1.0 | 0.5 | 0.42654694315586256 |
| 507.0 | 6.329398370441452e-2 | 1.0 | 0.5 | 0.1307715918497558 |
| 508.0 | 0.8324305296152746 | 1.0 | 0.5 | 3.5727145302718077 |
| 509.0 | 0.788341172013531 | 1.0 | 0.5 | 3.105559205156182 |
| 510.0 | 0.9968069620813552 | 1.0 | 0.5 | 11.493564979531474 |
| 511.0 | 0.5738675981141458 | 1.0 | 0.5 | 1.70601035690584 |
| 512.0 | 2.1924901743684888e-2 | 1.0 | 0.5 | 4.433764862438196e-2 |
| 513.0 | 0.31475857342097047 | 1.0 | 0.5 | 0.755968110508673 |
| 514.0 | 0.5840924030390661 | 1.0 | 0.5 | 1.7545843321676697 |
| 515.0 | 0.7770937124350712 | 1.0 | 0.5 | 3.0020076619607927 |
| 516.0 | 0.7130309607190969 | 1.0 | 0.5 | 2.496761892223379 |
| 517.0 | 0.3991683439699586 | 1.0 | 0.5 | 1.0188809802465286 |
| 518.0 | 0.48509787688675254 | 1.0 | 0.5 | 1.3275568971757934 |
| 519.0 | 0.45169086393350866 | 1.0 | 0.5 | 1.2018320683571915 |
| 520.0 | 0.12902719463817103 | 1.0 | 0.5 | 0.2762890498682689 |
| 521.0 | 0.2592134704552379 | 1.0 | 0.5 | 0.6000855589507411 |
| 522.0 | 0.2813001484016546 | 1.0 | 0.5 | 0.660622921987986 |
| 523.0 | 0.3762566475369137 | 1.0 | 0.5 | 0.9440325786940167 |
| 524.0 | 0.9267694189625785 | 1.0 | 0.5 | 5.228284343045507 |
| 525.0 | 0.15197347800388006 | 1.0 | 0.5 | 0.32968673548099053 |
| 526.0 | 0.14593597548755854 | 1.0 | 0.5 | 0.3154982356972103 |
| 527.0 | 0.35991105880863383 | 1.0 | 0.5 | 0.8922962833448616 |
| 528.0 | 0.3071108301936344 | 1.0 | 0.5 | 0.7337704414228866 |
| 529.0 | 0.18406488587857806 | 1.0 | 0.5 | 0.40684088837547255 |
| 530.0 | 0.5136711150110314 | 1.0 | 0.5 | 1.4417403317355608 |
| 531.0 | 0.6056599490894785 | 1.0 | 0.5 | 1.8610833370818218 |
| 532.0 | 0.44302266378392197 | 1.0 | 0.5 | 1.1704614578045645 |
| 533.0 | 0.7453946147476727 | 1.0 | 0.5 | 2.736080882533261 |
| 534.0 | 0.35502683979054106 | 1.0 | 0.5 | 0.8770931502610587 |
| 535.0 | 0.8797839338461947 | 1.0 | 0.5 | 4.236929207935352 |
| 536.0 | 6.238712029644411e-2 | 1.0 | 0.5 | 0.12883624673709432 |
| 537.0 | 0.9424758304767943 | 1.0 | 0.5 | 5.711100159925468 |
| 538.0 | 0.5704103694439081 | 1.0 | 0.5 | 1.689849747036119 |
| 539.0 | 0.9158448954522559 | 1.0 | 0.5 | 4.950187400162322 |
| 540.0 | 0.8873811703242918 | 1.0 | 0.5 | 4.367492702015337 |
| 541.0 | 0.6216382152888731 | 1.0 | 0.5 | 1.9438088773652653 |
| 542.0 | 0.48827370203072595 | 1.0 | 0.5 | 1.3399307423143256 |
| 543.0 | 0.4572473011375716 | 1.0 | 0.5 | 1.2222029953319737 |
| 544.0 | 3.967393021768029e-2 | 1.0 | 0.5 | 8.096479233410842e-2 |
| 545.0 | 0.29505351687188286 | 1.0 | 0.5 | 0.6992667790155687 |
| 546.0 | 0.5639878272499856 | 1.0 | 0.5 | 1.6601702337428519 |
| 547.0 | 0.48965142848521204 | 1.0 | 0.5 | 1.3453226263340796 |
| 548.0 | 0.8749613569725964 | 1.0 | 0.5 | 4.15826489047167 |
| 549.0 | 0.9256239598413039 | 1.0 | 0.5 | 5.19724285977426 |
| 550.0 | 0.7569605924580269 | 1.0 | 0.5 | 2.8290633556705096 |
| 551.0 | 0.3844172120051691 | 1.0 | 0.5 | 0.9703716742615802 |
| 552.0 | 0.1362858511639865 | 1.0 | 0.5 | 0.29302682234878386 |
| 553.0 | 0.37983323803435165 | 1.0 | 0.5 | 0.9555337323937093 |
| 554.0 | 0.26566735487098103 | 1.0 | 0.5 | 0.6175863160615908 |
| 555.0 | 0.36961293921650573 | 1.0 | 0.5 | 0.9228425321120206 |
| 556.0 | 9.63913755811967e-2 | 1.0 | 0.5 | 0.2027178998486325 |
| 557.0 | 0.18788147800222665 | 1.0 | 0.5 | 0.4162179728411676 |
| 558.0 | 0.26504942094508055 | 1.0 | 0.5 | 0.6159040428220617 |
| 559.0 | 0.739578089934357 | 1.0 | 0.5 | 2.6909044643005315 |
| 560.0 | 0.6457524310661318 | 1.0 | 0.5 | 2.075518526013794 |
| 561.0 | 0.9092071659989257 | 1.0 | 0.5 | 4.798349834364611 |
| 562.0 | 0.210969342425946 | 1.0 | 0.5 | 0.4739002053005348 |
| 563.0 | 0.9875371965277739 | 1.0 | 0.5 | 8.770013586320161 |
| 564.0 | 5.026001823606596e-2 | 1.0 | 0.5 | 0.10313407051509839 |
| 565.0 | 0.12100530799389475 | 1.0 | 0.5 | 0.2579528399771316 |
| 566.0 | 0.5890018173984741 | 1.0 | 0.5 | 1.7783329727795534 |
| 567.0 | 0.7224532694248171 | 1.0 | 0.5 | 2.563531923007902 |
| 568.0 | 0.8883622267112601 | 1.0 | 0.5 | 4.384991631947751 |
| 569.0 | 1.997234871111797e-2 | 1.0 | 0.5 | 4.0348984229344e-2 |
| 570.0 | 0.2212019516532856 | 1.0 | 0.5 | 0.5000070229243507 |
| 571.0 | 0.4273320266042193 | 1.0 | 0.5 | 1.1148983665533385 |
| 572.0 | 0.9981606891187609 | 1.0 | 0.5 | 12.596728597154938 |
| 573.0 | 0.24173972097342367 | 1.0 | 0.5 | 0.5534571525106269 |
| 574.0 | 0.30996923292210077 | 1.0 | 0.5 | 0.7420381848339302 |
| 575.0 | 0.5586292644735815 | 1.0 | 0.5 | 1.635740173145147 |
| 576.0 | 0.5710479605847417 | 1.0 | 0.5 | 1.6928203250772282 |
| 577.0 | 0.7716668116970227 | 1.0 | 0.5 | 2.953898729115331 |
| 578.0 | 0.1347753271289409 | 1.0 | 0.5 | 0.2895321367059855 |
| 579.0 | 0.7338594799945437 | 1.0 | 0.5 | 2.647461677978153 |
| 580.0 | 0.11653928663743118 | 1.0 | 0.5 | 0.2478169105193333 |
| 581.0 | 0.939970706008818 | 1.0 | 0.5 | 5.6258452054414265 |
| 582.0 | 0.6523955046706064 | 1.0 | 0.5 | 2.1133799063928125 |
| 583.0 | 0.8032152138335183 | 1.0 | 0.5 | 3.2512892068337553 |
| 584.0 | 0.6707576127357098 | 1.0 | 0.5 | 2.22192212014176 |
| 585.0 | 0.706492182007378 | 1.0 | 0.5 | 2.451702005911791 |
| 586.0 | 0.7647771000645677 | 1.0 | 0.5 | 2.894443408052288 |
| 587.0 | 0.8173666289040515 | 1.0 | 0.5 | 3.400549144677044 |
| 588.0 | 0.21872300084295448 | 1.0 | 0.5 | 0.49365103921530656 |
| 589.0 | 0.7310001086753175 | 1.0 | 0.5 | 2.626088606755685 |
| 590.0 | 0.8697372961374548 | 1.0 | 0.5 | 4.076404137303028 |
| 591.0 | 0.16400157324713815 | 1.0 | 0.5 | 0.35825709554754664 |
| 592.0 | 0.443313209241608 | 1.0 | 0.5 | 1.1715050236598006 |
| 593.0 | 0.8053657385685586 | 1.0 | 0.5 | 3.2732661278566844 |
| 594.0 | 0.30079618388942353 | 1.0 | 0.5 | 0.7156259936633084 |
| 595.0 | 0.45365879994299696 | 1.0 | 0.5 | 1.2090231797639022 |
| 596.0 | 0.8069809893677921 | 1.0 | 0.5 | 3.2899331884862204 |
| 597.0 | 0.8245884511775761 | 1.0 | 0.5 | 3.4812407168767363 |
| 598.0 | 0.41234621682526684 | 1.0 | 0.5 | 1.063234617242673 |
| 599.0 | 0.7968250346184004 | 1.0 | 0.5 | 3.1873755454599793 |
| 600.0 | 0.3927417451741644 | 1.0 | 0.5 | 0.9976022348148476 |
| 601.0 | 0.2728256265562098 | 1.0 | 0.5 | 0.6371779535572102 |
| 602.0 | 8.203703710226784e-2 | 1.0 | 0.5 | 0.1711964692057045 |
| 603.0 | 0.37776869105901545 | 1.0 | 0.5 | 0.9488867520931888 |
| 604.0 | 0.629411417011517 | 1.0 | 0.5 | 1.9853255448725573 |
| 605.0 | 0.7501910378551001 | 1.0 | 0.5 | 2.774117609305619 |
| 606.0 | 0.5996333900313177 | 1.0 | 0.5 | 1.8307492534099181 |
| 607.0 | 6.970492186242305e-2 | 1.0 | 0.5 | 0.14450690968030883 |
| 608.0 | 0.8940723635978487 | 1.0 | 0.5 | 4.489998186900931 |
| 609.0 | 0.6914380476986998 | 1.0 | 0.5 | 2.3516652759309613 |
| 610.0 | 0.3650550811756098 | 1.0 | 0.5 | 0.9084340517211708 |
| 611.0 | 6.128389132423373e-2 | 1.0 | 0.5 | 0.12648435832908939 |
| 612.0 | 0.7680345331232672 | 1.0 | 0.5 | 2.9223335361285874 |
| 613.0 | 0.23058140936561866 | 1.0 | 0.5 | 0.5242402528938145 |
| 614.0 | 0.18527936511039267 | 1.0 | 0.5 | 0.40982000756014936 |
| 615.0 | 0.28710359124883345 | 1.0 | 0.5 | 0.676838316784801 |
| 616.0 | 0.2108511727487865 | 1.0 | 0.5 | 0.4736006964588179 |
| 617.0 | 0.4624966724507077 | 1.0 | 0.5 | 1.241640656420202 |
| 618.0 | 0.9811961480020145 | 1.0 | 0.5 | 7.947387073248349 |
| 619.0 | 0.8245811182215507 | 1.0 | 0.5 | 3.4811571100355176 |
| 620.0 | 0.8168888824501829 | 1.0 | 0.5 | 3.3953242213722077 |
| 621.0 | 0.6208021859846258 | 1.0 | 0.5 | 1.9393945466905502 |
| 622.0 | 0.6514815007857316 | 1.0 | 0.5 | 2.108127935592991 |
| 623.0 | 0.8002695403068458 | 1.0 | 0.5 | 3.221573045869622 |
| 624.0 | 0.44461502006124476 | 1.0 | 0.5 | 1.176187496315193 |
| 625.0 | 0.24944701714221795 | 1.0 | 0.5 | 0.5738900673091492 |
| 626.0 | 0.15074825367641687 | 1.0 | 0.5 | 0.32679923126166255 |
| 627.0 | 0.48159464793185214 | 1.0 | 0.5 | 1.3139956195836684 |
| 628.0 | 0.7293846186875164 | 1.0 | 0.5 | 2.614113446702638 |
| 629.0 | 0.22027782974542087 | 1.0 | 0.5 | 0.49763522946247984 |
| 630.0 | 0.20798244662851184 | 1.0 | 0.5 | 0.46634344813095197 |
| 631.0 | 0.6919106866218325 | 1.0 | 0.5 | 2.354731119087316 |
| 632.0 | 0.9720879966592072 | 1.0 | 0.5 | 7.1573969108149464 |
| 633.0 | 0.9555225762947214 | 1.0 | 0.5 | 6.2255471020245 |
| 634.0 | 0.9266176357074408 | 1.0 | 0.5 | 5.22414328144902 |
| 635.0 | 0.3345233531787639 | 1.0 | 0.5 | 0.8145034658807747 |
| 636.0 | 0.718429616730576 | 1.0 | 0.5 | 2.5347456665564865 |
| 637.0 | 0.8878295863454977 | 1.0 | 0.5 | 4.375472027171012 |
| 638.0 | 0.3092083755044568 | 1.0 | 0.5 | 0.7398341142762942 |
| 639.0 | 0.7848160471899103 | 1.0 | 0.5 | 3.0725240444024844 |
| 640.0 | 0.4053789118552892 | 1.0 | 0.5 | 1.0396618058885414 |
| 641.0 | 0.7411589342475445 | 1.0 | 0.5 | 2.7030821027672483 |
| 642.0 | 0.45881127046875336 | 1.0 | 0.5 | 1.2279744157261765 |
| 643.0 | 0.22682375665456067 | 1.0 | 0.5 | 0.5144965144587033 |
| 644.0 | 0.16776368155499566 | 1.0 | 0.5 | 0.3672776837982627 |
| 645.0 | 0.5154869010668474 | 1.0 | 0.5 | 1.449221624070085 |
| 646.0 | 0.34454745235530915 | 1.0 | 0.5 | 0.8448587389647154 |
| 647.0 | 0.7272962306454143 | 1.0 | 0.5 | 2.5987383337541456 |
| 648.0 | 0.9326344100560882 | 1.0 | 0.5 | 5.395241852776035 |
| 649.0 | 0.29777872274027883 | 1.0 | 0.5 | 0.7070134297056129 |
| 650.0 | 0.9174126215561729 | 1.0 | 0.5 | 4.987796826948644 |
| 651.0 | 4.5376575536940744e-2 | 1.0 | 0.5 | 9.28766723998495e-2 |
| 652.0 | 0.4810458793989424 | 1.0 | 0.5 | 1.3118795986726604 |
| 653.0 | 0.2950958156948137 | 1.0 | 0.5 | 0.6993867883865484 |
| 654.0 | 0.2212183176044329 | 1.0 | 0.5 | 0.5000490521080256 |
| 655.0 | 0.8206110378697538 | 1.0 | 0.5 | 3.436397715675071 |
| 656.0 | 0.5629205916000906 | 1.0 | 0.5 | 1.6552807756173478 |
| 657.0 | 0.6994457934362445 | 1.0 | 0.5 | 2.4042543067509254 |
| 658.0 | 0.9830737705685453 | 1.0 | 0.5 | 8.15778164570834 |
| 659.0 | 0.8325491011982105 | 1.0 | 0.5 | 3.5741302243464643 |
| 660.0 | 0.2556320014497683 | 1.0 | 0.5 | 0.5904394894534792 |
| 661.0 | 0.5735719811333211 | 1.0 | 0.5 | 1.7046233960032402 |
| 662.0 | 0.3675775710486384 | 1.0 | 0.5 | 0.916395415751471 |
| 663.0 | 0.34003954250576196 | 1.0 | 0.5 | 0.8311507172880653 |
| 664.0 | 0.11876686109163148 | 1.0 | 0.5 | 0.2528661163846483 |
| 665.0 | 0.4006303818268926 | 1.0 | 0.5 | 1.0237536248988528 |
| 666.0 | 0.6505245015357007 | 1.0 | 0.5 | 2.10264364860553 |
| 667.0 | 0.10086413330967303 | 1.0 | 0.5 | 0.21264225003430867 |
| 668.0 | 0.5732670526062165 | 1.0 | 0.5 | 1.7031937546945186 |
| 669.0 | 0.11878163120957308 | 1.0 | 0.5 | 0.25289963814199007 |
| 670.0 | 0.10072655638961314 | 1.0 | 0.5 | 0.21233625313017732 |
| 671.0 | 0.5618155656093103 | 1.0 | 0.5 | 1.6502307483050531 |
| 672.0 | 0.4500194541428453 | 1.0 | 0.5 | 1.1957447451000092 |
| 673.0 | 0.3981179573374246 | 1.0 | 0.5 | 1.0153875905870227 |
| 674.0 | 0.3056380605496083 | 1.0 | 0.5 | 0.7295238556749573 |
| 675.0 | 0.30445908406220024 | 1.0 | 0.5 | 0.7261308796389632 |
| 676.0 | 0.3632627258496999 | 1.0 | 0.5 | 0.9027963019039356 |
| 677.0 | 0.9360322656138116 | 1.0 | 0.5 | 5.49875294592944 |
| 678.0 | 9.829392285480298e-2 | 1.0 | 0.5 | 0.20693333769178202 |
| 679.0 | 0.16857186470900742 | 1.0 | 0.5 | 0.3692208237524054 |
| 680.0 | 0.6851865756088779 | 1.0 | 0.5 | 2.3115502383155535 |
| 681.0 | 6.0746870332459846e-2 | 1.0 | 0.5 | 0.12534052488294534 |
| 682.0 | 0.13633830378369793 | 1.0 | 0.5 | 0.29314828432216455 |
| 683.0 | 7.392456123744273e-2 | 1.0 | 0.5 | 0.15359916061613507 |
| 684.0 | 0.5086610045902572 | 1.0 | 0.5 | 1.4212419421338762 |
| 685.0 | 6.49975666739836e-2 | 1.0 | 0.5 | 0.13441229441823688 |
| 686.0 | 0.5965150506093662 | 1.0 | 0.5 | 1.8152321842298589 |
| 687.0 | 0.4605739048163946 | 1.0 | 0.5 | 1.2344989825581794 |
| 688.0 | 6.864473076963751e-2 | 1.0 | 0.5 | 0.14222894978542783 |
| 689.0 | 0.8344360964556317 | 1.0 | 0.5 | 3.596796069113207 |
| 690.0 | 8.732363913477414e-2 | 1.0 | 0.5 | 0.18274788003816578 |
| 691.0 | 0.21142843578856352 | 1.0 | 0.5 | 0.4750642335181355 |
| 692.0 | 0.7222864812254323 | 1.0 | 0.5 | 2.56233040927018 |
| 693.0 | 0.8184225707114926 | 1.0 | 0.5 | 3.41214621719645 |
| 694.0 | 0.21034070512424763 | 1.0 | 0.5 | 0.4723073977097867 |
| 695.0 | 0.3162647360277784 | 1.0 | 0.5 | 0.7603689545111227 |
| 696.0 | 0.790802361192664 | 1.0 | 0.5 | 3.1289516672537636 |
| 697.0 | 0.13344125555833664 | 1.0 | 0.5 | 0.28645075407773934 |
| 698.0 | 0.7007503099963927 | 1.0 | 0.5 | 2.4129539409113554 |
| 699.0 | 0.9187168349404677 | 1.0 | 0.5 | 5.019632711418354 |
| 700.0 | 0.4599361344660652 | 1.0 | 0.5 | 1.2321357538196342 |
| 701.0 | 0.27682148640453996 | 1.0 | 0.5 | 0.6481983610575905 |
| 702.0 | 1.800784163218272e-3 | 1.0 | 0.5 | 3.604815048388172e-3 |
| 703.0 | 0.7228478637519237 | 1.0 | 0.5 | 2.566377390466142 |
| 704.0 | 0.2985911645520416 | 1.0 | 0.5 | 0.7093286889612735 |
| 705.0 | 0.17315276773821076 | 1.0 | 0.5 | 0.38027065243919844 |
| 706.0 | 0.14367496112886713 | 1.0 | 0.5 | 0.3102105132154968 |
| 707.0 | 0.8032824900526222 | 1.0 | 0.5 | 3.2519730780123863 |
| 708.0 | 0.9263217228455575 | 1.0 | 0.5 | 5.216094540240102 |
| 709.0 | 0.14390022398042068 | 1.0 | 0.5 | 0.3107366977250743 |
| 710.0 | 0.16040391518009045 | 1.0 | 0.5 | 0.349668708391516 |
| 711.0 | 0.23371462343815053 | 1.0 | 0.5 | 0.5324012487287254 |
| 712.0 | 0.5919728166898646 | 1.0 | 0.5 | 1.7928429620744737 |
| 713.0 | 0.103304290572939 | 1.0 | 0.5 | 0.2180774117665795 |
| 714.0 | 0.9472027518538018 | 1.0 | 0.5 | 5.8825924161427 |
| 715.0 | 2.606352915311938e-2 | 1.0 | 0.5 | 5.2818404940095244e-2 |
| 716.0 | 0.3644003382289155 | 1.0 | 0.5 | 0.9063727529217371 |
| 717.0 | 0.8078186466096073 | 1.0 | 0.5 | 3.298631607703358 |
| 718.0 | 0.945005788294678 | 1.0 | 0.5 | 5.801054682018765 |
| 719.0 | 0.3026751927307447 | 1.0 | 0.5 | 0.7210079384190151 |
| 720.0 | 0.13192709639852174 | 1.0 | 0.5 | 0.28295915504898594 |
| 721.0 | 0.2566043774869836 | 1.0 | 0.5 | 0.5930538192083015 |
| 722.0 | 0.10698594605754641 | 1.0 | 0.5 | 0.22630592066398597 |
| 723.0 | 0.6145249789812167 | 1.0 | 0.5 | 1.9065577687955066 |
| 724.0 | 8.930234303466789e-2 | 1.0 | 0.5 | 0.18708863439196044 |
| 725.0 | 0.5078727259176272 | 1.0 | 0.5 | 1.4180358175685772 |
| 726.0 | 0.6074906579312169 | 1.0 | 0.5 | 1.87038988118759 |
| 727.0 | 0.4328926795524597 | 1.0 | 0.5 | 1.1344134309597795 |
| 728.0 | 0.9199649411490993 | 1.0 | 0.5 | 5.0505810093383765 |
| 729.0 | 0.9554407909442231 | 1.0 | 0.5 | 6.221872867230832 |
| 730.0 | 0.25047456653555367 | 1.0 | 0.5 | 0.5766300562133851 |
| 731.0 | 0.8421247486490246 | 1.0 | 0.5 | 3.6919002124484552 |
| 732.0 | 7.259025420929721e-2 | 1.0 | 0.5 | 0.15071959671184512 |
| 733.0 | 0.11620186500134255 | 1.0 | 0.5 | 0.24705319299034392 |
| 734.0 | 0.30850615207588095 | 1.0 | 0.5 | 0.7378020492564494 |
| 735.0 | 0.1883626739567803 | 1.0 | 0.5 | 0.4174033627979661 |
| 736.0 | 0.6623776199438104 | 1.0 | 0.5 | 2.171654453643791 |
| 737.0 | 0.11290896034135722 | 1.0 | 0.5 | 0.2396153284366351 |
| 738.0 | 0.7422642656298815 | 1.0 | 0.5 | 2.7116410087728458 |
| 739.0 | 0.7587318490143916 | 1.0 | 0.5 | 2.8436926086943526 |
| 740.0 | 0.530336944600492 | 1.0 | 0.5 | 1.5114794895629295 |
| 741.0 | 0.6223687487469611 | 1.0 | 0.5 | 1.9476741705662395 |
| 742.0 | 0.5630377543656311 | 1.0 | 0.5 | 1.6558169640962446 |
| 743.0 | 0.6774789767908294 | 1.0 | 0.5 | 2.2631739132509616 |
| 744.0 | 0.7134140595880138 | 1.0 | 0.5 | 2.499433642548651 |
| 745.0 | 0.10002976757404214 | 1.0 | 0.5 | 0.2107871825741759 |
| 746.0 | 0.567848281252265 | 1.0 | 0.5 | 1.677957103179304 |
| 747.0 | 0.4060863958044679 | 1.0 | 0.5 | 1.0420428353605442 |
| 748.0 | 9.50513656846318e-2 | 1.0 | 0.5 | 0.19975418911112916 |
| 749.0 | 0.16631249236543122 | 1.0 | 0.5 | 0.36379327584656745 |
| 750.0 | 0.8411217147036525 | 1.0 | 0.5 | 3.6792337423769124 |
| 751.0 | 0.8261179212585096 | 1.0 | 0.5 | 3.49875583579596 |
| 752.0 | 2.270068052664842e-2 | 1.0 | 0.5 | 4.5924615942012824e-2 |
| 753.0 | 0.794409914691346 | 1.0 | 0.5 | 3.163741939541678 |
| 754.0 | 9.042111886289728e-2 | 1.0 | 0.5 | 0.18954710912519077 |
| 755.0 | 0.8703489330265705 | 1.0 | 0.5 | 4.0858170747733435 |
| 756.0 | 0.4310407173673936 | 1.0 | 0.5 | 1.1278928138744269 |
| 757.0 | 0.3363067143343986 | 1.0 | 0.5 | 0.819870310803085 |
| 758.0 | 0.5130758701244038 | 1.0 | 0.5 | 1.4392939176921202 |
| 759.0 | 0.7908192098998476 | 1.0 | 0.5 | 3.1291127530653498 |
| 760.0 | 0.13285818054236243 | 1.0 | 0.5 | 0.28510548131242014 |
| 761.0 | 0.20374760571336736 | 1.0 | 0.5 | 0.45567813029380005 |
| 762.0 | 0.5394576445781261 | 1.0 | 0.5 | 1.5507009009747217 |
| 763.0 | 6.022815649686475e-2 | 1.0 | 0.5 | 0.12423630571358278 |
| 764.0 | 6.998298713513784e-2 | 1.0 | 0.5 | 0.1451047991981602 |
| 765.0 | 0.8970007092256013 | 1.0 | 0.5 | 4.546066352923313 |
| 766.0 | 0.4229521552684008 | 1.0 | 0.5 | 1.0996601921991296 |
| 767.0 | 0.8670835830456738 | 1.0 | 0.5 | 4.036069584532981 |
| 768.0 | 0.4225403174878275 | 1.0 | 0.5 | 1.0982333057100466 |
| 769.0 | 0.7514278370027265 | 1.0 | 0.5 | 2.784044162493016 |
| 770.0 | 0.3166625910490667 | 1.0 | 0.5 | 0.7615330624342808 |
| 771.0 | 0.35844011299274825 | 1.0 | 0.5 | 0.8877054892696948 |
| 772.0 | 0.6478614003000068 | 1.0 | 0.5 | 2.08746086347037 |
| 773.0 | 0.7201698321036184 | 1.0 | 0.5 | 2.547144806123709 |
| 774.0 | 0.36834307496040686 | 1.0 | 0.5 | 0.9188177447070995 |
| 775.0 | 0.8093749686530289 | 1.0 | 0.5 | 3.3148939343546955 |
| 776.0 | 0.11152824342387613 | 1.0 | 0.5 | 0.2365048393033555 |
| 777.0 | 0.8664817488175403 | 1.0 | 0.5 | 4.027034195017947 |
| 778.0 | 0.5335069631739435 | 1.0 | 0.5 | 1.525024370448048 |
| 779.0 | 0.3938842132094552 | 1.0 | 0.5 | 1.0013684877054532 |
| 780.0 | 0.10183253557450678 | 1.0 | 0.5 | 0.21479748413816208 |
| 781.0 | 8.053929148343719e-2 | 1.0 | 0.5 | 0.16793593441701318 |
| 782.0 | 0.6987324536890543 | 1.0 | 0.5 | 2.39951310171572 |
| 783.0 | 0.9652504478720477 | 1.0 | 0.5 | 6.719177190917287 |
| 784.0 | 8.352182889441673e-2 | 1.0 | 0.5 | 0.17443405931174336 |
| 785.0 | 0.6848311037778758 | 1.0 | 0.5 | 2.309293210731784 |
| 786.0 | 0.8753831237435886 | 1.0 | 0.5 | 4.165022476660053 |
| 787.0 | 0.5017501580403889 | 1.0 | 0.5 | 1.3933072741604589 |
| 788.0 | 0.5164740417097261 | 1.0 | 0.5 | 1.4533005544713893 |
| 789.0 | 5.34146726412843e-3 | 1.0 | 0.5 | 1.0711567808793232e-2 |
| 790.0 | 0.3318023439651666 | 1.0 | 0.5 | 0.8063425138905206 |
| 791.0 | 0.8980158281288398 | 1.0 | 0.5 | 4.565875310945801 |
| 792.0 | 0.3077912787425361 | 1.0 | 0.5 | 0.7357354970569514 |
| 793.0 | 8.244870505377133e-2 | 1.0 | 0.5 | 0.1720935866356492 |
| 794.0 | 0.9525163323404254 | 1.0 | 0.5 | 6.09473893162687 |
| 795.0 | 0.8150156026591003 | 1.0 | 0.5 | 3.37496759231672 |
| 796.0 | 0.6942073379694466 | 1.0 | 0.5 | 2.3696959635020733 |
| 797.0 | 0.3064154871080699 | 1.0 | 0.5 | 0.7317643647045484 |
| 798.0 | 0.6766043921266462 | 1.0 | 0.5 | 2.257757826055945 |
| 799.0 | 0.8850082431583656 | 1.0 | 0.5 | 4.325789665651913 |
| 800.0 | 0.390824501859813 | 1.0 | 0.5 | 0.9912977570009057 |
| 801.0 | 0.9277232680571199 | 1.0 | 0.5 | 5.254506056261732 |
| 802.0 | 0.9515352841551716 | 1.0 | 0.5 | 6.0538385083107515 |
| 803.0 | 0.931339195694947 | 1.0 | 0.5 | 5.3571535534070165 |
| 804.0 | 0.41693501872311356 | 1.0 | 0.5 | 1.078913277353363 |
| 805.0 | 0.9925072396884476 | 1.0 | 0.5 | 9.787636032854072 |
| 806.0 | 0.6270345104129288 | 1.0 | 0.5 | 1.9725387696659187 |
| 807.0 | 0.9026186077418837 | 1.0 | 0.5 | 4.658240262325699 |
| 808.0 | 0.9572829405210631 | 1.0 | 0.5 | 6.306313838242294 |
| 809.0 | 0.3836448542598979 | 1.0 | 0.5 | 0.9678638925777285 |
| 810.0 | 0.9149835758010022 | 1.0 | 0.5 | 4.929821630573945 |
| 811.0 | 0.9650769640332738 | 1.0 | 0.5 | 6.70921722232391 |
| 812.0 | 0.8333633906893451 | 1.0 | 0.5 | 3.5838796592561692 |
| 813.0 | 0.4659441674707303 | 1.0 | 0.5 | 1.254509780378417 |
| 814.0 | 0.6644565585399186 | 1.0 | 0.5 | 2.1840076964202977 |
| 815.0 | 0.5950530445808047 | 1.0 | 0.5 | 1.8079983894541376 |
| 816.0 | 0.6963681256453612 | 1.0 | 0.5 | 2.383878501957269 |
| 817.0 | 0.7577963228381738 | 1.0 | 0.5 | 2.835952531284403 |
| 818.0 | 5.205609640929987e-2 | 1.0 | 0.5 | 0.10691990381092617 |
| 819.0 | 0.266407912351019 | 1.0 | 0.5 | 0.6196042874911304 |
| 820.0 | 0.2334514658552196 | 1.0 | 0.5 | 0.5317145270071396 |
| 821.0 | 0.3750267027666355 | 1.0 | 0.5 | 0.9400927091701334 |
| 822.0 | 0.6114309357827568 | 1.0 | 0.5 | 1.8905687070019104 |
| 823.0 | 0.15551257370829796 | 1.0 | 0.5 | 0.338050863566619 |
| 824.0 | 0.23813670768097417 | 1.0 | 0.5 | 0.5439762915924459 |
| 825.0 | 0.870266707449991 | 1.0 | 0.5 | 4.084549063402345 |
| 826.0 | 3.154053223157938e-2 | 1.0 | 0.5 | 6.409729506779938e-2 |
| 827.0 | 0.1696763468222191 | 1.0 | 0.5 | 0.3718794212240955 |
| 828.0 | 0.9382537256649148 | 1.0 | 0.5 | 5.5694432798975 |
| 829.0 | 0.5690815690817298 | 1.0 | 0.5 | 1.6836729243990005 |
| 830.0 | 0.40173525398781396 | 1.0 | 0.5 | 1.027443807837522 |
| 831.0 | 0.37414920622824466 | 1.0 | 0.5 | 0.9372865697980035 |
| 832.0 | 0.8470235035676409 | 1.0 | 0.5 | 3.75494197495865 |
| 833.0 | 0.1639959478716596 | 1.0 | 0.5 | 0.35824363773142587 |
| 834.0 | 0.8417875076966232 | 1.0 | 0.5 | 3.6876325230218985 |
| 835.0 | 4.4800017902082656e-2 | 1.0 | 0.5 | 9.166911017102182e-2 |
| 836.0 | 8.371841878440967e-2 | 1.0 | 0.5 | 0.17486311694572496 |
| 837.0 | 0.792681678313694 | 1.0 | 0.5 | 3.1469997619491537 |
| 838.0 | 0.70931724616291 | 1.0 | 0.5 | 2.4710455990059654 |
| 839.0 | 0.9536753385934513 | 1.0 | 0.5 | 6.14416163184868 |
| 840.0 | 0.9602885160673758 | 1.0 | 0.5 | 6.452229730589149 |
| 841.0 | 0.7554874920826832 | 1.0 | 0.5 | 2.8169776284993024 |
| 842.0 | 0.13372214044518893 | 1.0 | 0.5 | 0.28709913578589247 |
| 843.0 | 0.5137668062151739 | 1.0 | 0.5 | 1.442133895114351 |
| 844.0 | 0.7646430480436962 | 1.0 | 0.5 | 2.893303945544842 |
| 845.0 | 0.737797471240314 | 1.0 | 0.5 | 2.677276126613299 |
| 846.0 | 0.2757355366840477 | 1.0 | 0.5 | 0.64519734494416 |
| 847.0 | 0.9070120314664122 | 1.0 | 0.5 | 4.750570329609125 |
| 848.0 | 0.8891902056378072 | 1.0 | 0.5 | 4.399880223610048 |
| 849.0 | 0.6345074545436008 | 1.0 | 0.5 | 2.0130187909368447 |
| 850.0 | 0.7278594124350969 | 1.0 | 0.5 | 2.602872960335896 |
| 851.0 | 0.7746614730325766 | 1.0 | 0.5 | 2.9803028864018035 |
| 852.0 | 0.1615769961852126 | 1.0 | 0.5 | 0.35246505607116085 |
| 853.0 | 0.791392456147658 | 1.0 | 0.5 | 3.134601145736619 |
| 854.0 | 0.6680598448264417 | 1.0 | 0.5 | 2.2056011636299084 |
| 855.0 | 8.13464244932427e-3 | 1.0 | 0.5 | 1.63358183694174e-2 |
| 856.0 | 0.10662505504031572 | 1.0 | 0.5 | 0.2254978301028323 |
| 857.0 | 0.9560295924470773 | 1.0 | 0.5 | 6.248476853892508 |
| 858.0 | 0.852060175832956 | 1.0 | 0.5 | 3.821899362730954 |
| 859.0 | 0.9719898411238275 | 1.0 | 0.5 | 7.150376035205891 |
| 860.0 | 0.2712547072167808 | 1.0 | 0.5 | 0.6328620012818229 |
| 861.0 | 0.5288209915498573 | 1.0 | 0.5 | 1.5050343935333608 |
| 862.0 | 0.7664883160339533 | 1.0 | 0.5 | 2.909046329453154 |
| 863.0 | 0.19497241124386178 | 1.0 | 0.5 | 0.433757460808162 |
| 864.0 | 0.22833492954177637 | 1.0 | 0.5 | 0.5184093393054244 |
| 865.0 | 0.8948424632463157 | 1.0 | 0.5 | 4.504591406380232 |
| 866.0 | 0.8605530887700942 | 1.0 | 0.5 | 3.9401426296279958 |
| 867.0 | 0.15650870586231802 | 1.0 | 0.5 | 0.34041139633305906 |
| 868.0 | 0.45930982890306726 | 1.0 | 0.5 | 1.2298177217350685 |
| 869.0 | 0.3246506528569505 | 1.0 | 0.5 | 0.7850503413401291 |
| 870.0 | 0.9510363593524436 | 1.0 | 0.5 | 6.033354567822288 |
| 871.0 | 0.5038715294705464 | 1.0 | 0.5 | 1.4018407452622141 |
| 872.0 | 5.1203794293428584e-2 | 1.0 | 0.5 | 0.10512249958235921 |
| 873.0 | 0.5544890265721415 | 1.0 | 0.5 | 1.617066801324528 |
| 874.0 | 0.2614676505710716 | 1.0 | 0.5 | 0.6061807474664205 |
| 875.0 | 5.243774821209923e-2 | 1.0 | 0.5 | 0.10772528617673659 |
| 876.0 | 0.7971971719215974 | 1.0 | 0.5 | 3.191042124405132 |
| 877.0 | 0.7919800318693259 | 1.0 | 0.5 | 3.140242406520687 |
| 878.0 | 0.5219283825848018 | 1.0 | 0.5 | 1.4759894609704223 |
| 879.0 | 0.4275949818719734 | 1.0 | 0.5 | 1.1158169290356357 |
| 880.0 | 0.6781991679783914 | 1.0 | 0.5 | 2.267644917806484 |
| 881.0 | 0.8932373960711312 | 1.0 | 0.5 | 4.4742951285035835 |
| 882.0 | 0.5478472624532599 | 1.0 | 0.5 | 1.5874704826077597 |
| 883.0 | 0.5685711063530478 | 1.0 | 0.5 | 1.6813051416100346 |
| 884.0 | 0.9721023498460244 | 1.0 | 0.5 | 7.158425635147378 |
| 885.0 | 0.21573425270663737 | 1.0 | 0.5 | 0.486014705364895 |
| 886.0 | 0.35259191127848954 | 1.0 | 0.5 | 0.8695568868089019 |
| 887.0 | 0.3472690789117947 | 1.0 | 0.5 | 0.853180600685918 |
| 888.0 | 0.5447448280041739 | 1.0 | 0.5 | 1.573794399314694 |
| 889.0 | 0.6727997991506037 | 1.0 | 0.5 | 2.2343661208216705 |
| 890.0 | 0.8696740488652007 | 1.0 | 0.5 | 4.0754333003570125 |
| 891.0 | 0.8734188023436674 | 1.0 | 0.5 | 4.133742596048804 |
| 892.0 | 0.29463161413393557 | 1.0 | 0.5 | 0.6980701589988371 |
| 893.0 | 3.283064353893961e-2 | 1.0 | 0.5 | 6.676332583206068e-2 |
| 894.0 | 0.23328215590600432 | 1.0 | 0.5 | 0.5312728295896356 |
| 895.0 | 0.40670843024467607 | 1.0 | 0.5 | 1.044138629788592 |
| 896.0 | 0.8227097978732368 | 1.0 | 0.5 | 3.45993465796266 |
| 897.0 | 0.7693837137837822 | 1.0 | 0.5 | 2.9340000964909345 |
| 898.0 | 0.5297116607776882 | 1.0 | 0.5 | 1.5088185693552791 |
| 899.0 | 0.7754688592512937 | 1.0 | 0.5 | 2.98748173968772 |
| 900.0 | 0.3248143864258809 | 1.0 | 0.5 | 0.7855352856755076 |
| 901.0 | 0.9401865839181958 | 1.0 | 0.5 | 5.633050587988034 |
| 902.0 | 0.34780122634541877 | 1.0 | 0.5 | 0.8548117918995641 |
| 903.0 | 0.7284097775344618 | 1.0 | 0.5 | 2.6069217674085974 |
| 904.0 | 0.9414922145837621 | 1.0 | 0.5 | 5.677190899121933 |
| 905.0 | 0.9905747699450492 | 1.0 | 0.5 | 9.328730273865226 |
| 906.0 | 0.6248601140341811 | 1.0 | 0.5 | 1.9609125866553052 |
| 907.0 | 2.9604572975377663e-2 | 1.0 | 0.5 | 6.010326765544391e-2 |
| 908.0 | 5.571585670694823e-3 | 1.0 | 0.5 | 1.1174329696467535e-2 |
| 909.0 | 0.6109088431526207 | 1.0 | 0.5 | 1.8878832528951692 |
| 910.0 | 0.4546914044998336 | 1.0 | 0.5 | 1.2128068285824642 |
| 911.0 | 6.989233588498944e-2 | 1.0 | 0.5 | 0.1449098633396298 |
| 912.0 | 7.423451728270214e-2 | 1.0 | 0.5 | 0.1542686696318581 |
| 913.0 | 0.5259803352971397 | 1.0 | 0.5 | 1.4930129428654308 |
| 914.0 | 0.658351708925572 | 1.0 | 0.5 | 2.1479469194904803 |
| 915.0 | 0.7900569250260353 | 1.0 | 0.5 | 3.121837713127503 |
| 916.0 | 0.23734310603381692 | 1.0 | 0.5 | 0.5418940581763712 |
| 917.0 | 0.5669448736548646 | 1.0 | 0.5 | 1.6737804930156697 |
| 918.0 | 0.32179695871984115 | 1.0 | 0.5 | 0.7766171299190336 |
| 919.0 | 0.10866609186678144 | 1.0 | 0.5 | 0.23007233022421117 |
| 920.0 | 0.45705634966886644 | 1.0 | 0.5 | 1.2214994782912616 |
| 921.0 | 0.9167978760940737 | 1.0 | 0.5 | 4.972964807530681 |
| 922.0 | 0.3082714338000597 | 1.0 | 0.5 | 0.7371232913772323 |
| 923.0 | 0.47735097863544884 | 1.0 | 0.5 | 1.2976902549082674 |
| 924.0 | 0.4404786851833078 | 1.0 | 0.5 | 1.161347311537362 |
| 925.0 | 0.7939848260637378 | 1.0 | 0.5 | 3.1596109060410877 |
| 926.0 | 0.2412573034838612 | 1.0 | 0.5 | 0.5521851246673014 |
| 927.0 | 0.3000287671367394 | 1.0 | 0.5 | 0.7134320813856403 |
| 928.0 | 0.6396708208326259 | 1.0 | 0.5 | 2.0414745575053637 |
| 929.0 | 0.7579547542491935 | 1.0 | 0.5 | 2.83726120877795 |
| 930.0 | 0.3396814387067095 | 1.0 | 0.5 | 0.8300657835650016 |
| 931.0 | 0.2892910263130618 | 1.0 | 0.5 | 0.6829845053640449 |
| 932.0 | 0.9011216837211008 | 1.0 | 0.5 | 4.6277306266829745 |
| 933.0 | 0.8210499505161656 | 1.0 | 0.5 | 3.441297130493794 |
| 934.0 | 0.20855830013501853 | 1.0 | 0.5 | 0.4677981203133048 |
| 935.0 | 0.20067661109578638 | 1.0 | 0.5 | 0.4479793460879907 |
| 936.0 | 0.2783975695108384 | 1.0 | 0.5 | 0.6525618840897621 |
| 937.0 | 8.228356433882655e-2 | 1.0 | 0.5 | 0.171733659388771 |
| 938.0 | 0.6509731323562694 | 1.0 | 0.5 | 2.105212750180706 |
| 939.0 | 0.22847862352050963 | 1.0 | 0.5 | 0.5187817997542151 |
| 940.0 | 0.8395983631124934 | 1.0 | 0.5 | 3.6601487571349267 |
| 941.0 | 0.9743748330931115 | 1.0 | 0.5 | 7.328360656210449 |
| 942.0 | 0.9784624250877514 | 1.0 | 0.5 | 7.675912397820349 |
| 943.0 | 0.30092332380443143 | 1.0 | 0.5 | 0.7159896972722992 |
| 944.0 | 0.9500911854862217 | 1.0 | 0.5 | 5.995115296523221 |
| 945.0 | 8.677317380760863e-2 | 1.0 | 0.5 | 0.1815419774519955 |
| 946.0 | 0.7801060191136249 | 1.0 | 0.5 | 3.0292195076905384 |
| 947.0 | 0.8407117298539127 | 1.0 | 0.5 | 3.674079397023548 |
| 948.0 | 2.5783170671583644e-2 | 1.0 | 0.5 | 5.2242765469739556e-2 |
| 949.0 | 0.6060746119884648 | 1.0 | 0.5 | 1.8631875162923288 |
| 950.0 | 0.5958910047385527 | 1.0 | 0.5 | 1.8121412943134505 |
| 951.0 | 0.1746502894487323 | 1.0 | 0.5 | 0.3838961817736315 |
| 952.0 | 0.5840255212924214 | 1.0 | 0.5 | 1.7542627397268897 |
| 953.0 | 0.39662332502701947 | 1.0 | 0.5 | 1.010427217997472 |
| 954.0 | 0.8294609650651269 | 1.0 | 0.5 | 3.5375821291253438 |
| 955.0 | 0.6431589573123884 | 1.0 | 0.5 | 2.060929709875304 |
| 956.0 | 0.985945005032187 | 1.0 | 0.5 | 8.52955486531813 |
| 957.0 | 0.6701686585706427 | 1.0 | 0.5 | 2.218347683497157 |
| 958.0 | 0.9097930895031728 | 1.0 | 0.5 | 4.8112984836026484 |
| 959.0 | 0.9461308232792471 | 1.0 | 0.5 | 5.842393650220309 |
| 960.0 | 5.2337673573452204e-2 | 1.0 | 0.5 | 0.10751407186495446 |
| 961.0 | 0.8460764659780871 | 1.0 | 0.5 | 3.7425986644134888 |
| 962.0 | 0.10514312394105463 | 1.0 | 0.5 | 0.22218297702597922 |
| 963.0 | 0.6975701033741791 | 1.0 | 0.5 | 2.3918115501203125 |
| 964.0 | 0.9576315406611462 | 1.0 | 0.5 | 6.322702154160914 |
| 965.0 | 0.5756726043081537 | 1.0 | 0.5 | 1.714499924122977 |
| 966.0 | 0.14180795869433915 | 1.0 | 0.5 | 0.3058547603470779 |
| 967.0 | 0.476602852426136 | 1.0 | 0.5 | 1.2948294774050015 |
| 968.0 | 0.5735933728030692 | 1.0 | 0.5 | 1.7047237280891907 |
| 969.0 | 0.21074148987963792 | 1.0 | 0.5 | 0.47332273812264397 |
| 970.0 | 0.4040283383810781 | 1.0 | 0.5 | 1.0351243213329395 |
| 971.0 | 0.44809616679520103 | 1.0 | 0.5 | 1.188762926181943 |
| 972.0 | 0.49753260138877997 | 1.0 | 0.5 | 1.376449039078463 |
| 973.0 | 0.3981895324367999 | 1.0 | 0.5 | 1.0156254423601343 |
| 974.0 | 0.4631341447354761 | 1.0 | 0.5 | 1.2440140393123216 |
| 975.0 | 0.7621220718869883 | 1.0 | 0.5 | 2.8719952879613677 |
| 976.0 | 0.35491286990322435 | 1.0 | 0.5 | 0.8767397717764418 |
| 977.0 | 0.5624416059435056 | 1.0 | 0.5 | 1.6530902199238997 |
| 978.0 | 0.9048958001682184 | 1.0 | 0.5 | 4.705564296273839 |
| 979.0 | 0.5871353426885598 | 1.0 | 0.5 | 1.769270891992473 |
| 980.0 | 0.9552974642377905 | 1.0 | 0.5 | 6.215450101021099 |
| 981.0 | 0.11683294268351829 | 1.0 | 0.5 | 0.24848180677799192 |
| 982.0 | 0.804763553103361 | 1.0 | 0.5 | 3.2670878135466657 |
| 983.0 | 0.9017746463190758 | 1.0 | 0.5 | 4.640981825644075 |
| 984.0 | 0.3546593618234335 | 1.0 | 0.5 | 0.8759539607786164 |
| 985.0 | 0.8175329055993971 | 1.0 | 0.5 | 3.4023708537413193 |
| 986.0 | 0.9271651336840998 | 1.0 | 0.5 | 5.239121011041082 |
| 987.0 | 0.43755514801054285 | 1.0 | 0.5 | 1.150924381235035 |
| 988.0 | 0.12335439229044076 | 1.0 | 0.5 | 0.2633049287820745 |
| 989.0 | 0.3823717678509384 | 1.0 | 0.5 | 0.9637371372795477 |
| 990.0 | 0.7134192888551714 | 1.0 | 0.5 | 2.499470136417535 |
| 991.0 | 0.5559806916407091 | 1.0 | 0.5 | 1.6237744603995141 |
| 992.0 | 0.7947950806945909 | 1.0 | 0.5 | 3.167492385499269 |
| 993.0 | 0.8593443260366468 | 1.0 | 0.5 | 3.9228808076956767 |
| 994.0 | 0.9695468897099642 | 1.0 | 0.5 | 6.983134291741286 |
| 995.0 | 9.645307355937993e-2 | 1.0 | 0.5 | 0.20285446358422873 |
| 996.0 | 0.5759218175673997 | 1.0 | 0.5 | 1.7156748964373492 |
| 997.0 | 0.8948271404741518 | 1.0 | 0.5 | 4.504300002523024 |
| 998.0 | 0.8288317043606008 | 1.0 | 0.5 | 3.5302160428926017 |
| 999.0 | 0.7727026977235016 | 1.0 | 0.5 | 2.962992833506503 |
dfExpRand.describe().show() // look sensible
+-------+-----------------+--------------------+----+----+--------------------+
|summary| Id| rand| one|rate| expo_sample|
+-------+-----------------+--------------------+----+----+--------------------+
| count| 1000| 1000|1000|1000| 1000|
| mean| 499.5| 0.5060789944940257| 1.0| 0.5| 2.024647367046763|
| stddev|288.8194360957494| 0.28795893414434537| 0.0| 0.0| 1.9649069025265538|
| min| 0|6.282087728304298E-4| 1.0| 0.5|0.001256812357281...|
| max| 999| 0.9981606891187609| 1.0| 0.5| 12.596728597154938|
+-------+-----------------+--------------------+----+----+--------------------+
val expoSamplesDF = spark.range(1000000000).toDF("Id") // just make a DF of 100 row indices
.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
.withColumn("one",lit(1.0))
.withColumn("rate",lit(0.5))
.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand"))
expoSamplesDF: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
expoSamplesDF.describe().show()
+-------+--------------------+--------------------+----------+----------+--------------------+
|summary| Id| rand| one| rate| expo_sample|
+-------+--------------------+--------------------+----------+----------+--------------------+
| count| 1000000000| 1000000000|1000000000|1000000000| 1000000000|
| mean|4.9999999907595944E8| 0.49999521358240573| 1.0| 0.5| 1.9999814119383708|
| stddev|2.8867513473915565E8| 0.2886816216562552| 0.0| 0.0| 2.0000011916404206|
| min| 0|1.584746778249268...| 1.0| 0.5|3.169493556749679...|
| max| 999999999| 0.9999999996809935| 1.0| 0.5| 43.731619350261035|
+-------+--------------------+--------------------+----------+----------+--------------------+
Approximating Pi with Monte Carlo simulations
Uisng RDDs directly, let's estimate Pi.
//Calculate pi with Monte Carlo estimation
import scala.math.random
//make a very large unique set of 1 -> n
val partitions = 2
val n = math.min(100000L * partitions, Int.MaxValue).toInt
val xs = 1 until n
//split up n into the number of partitions we can use
val rdd = sc.parallelize(xs, partitions).setName("'N values rdd'")
//generate a random set of points within a 2x2 square
val sample = rdd.map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
(x, y)
}.setName("'Random points rdd'")
//points w/in the square also w/in the center circle of r=1
val inside = sample.filter { case (x, y) => (x * x + y * y < 1) }.setName("'Random points inside circle'")
val count = inside.count()
//Area(circle)/Area(square) = inside/n => pi=4*inside/n
println("Pi is roughly " + 4.0 * count / n)
Pi is roughly 3.14156
import scala.math.random
partitions: Int = 2
n: Int = 200000
xs: scala.collection.immutable.Range = Range 1 until 200000
rdd: org.apache.spark.rdd.RDD[Int] = 'N values rdd' ParallelCollectionRDD[39] at parallelize at command-2971213210276568:10
sample: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points rdd' MapPartitionsRDD[40] at map at command-2971213210276568:13
inside: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points inside circle' MapPartitionsRDD[41] at filter at command-2971213210276568:20
count: Long = 157078
Doing it in PySpark is just as easy. This may be needed if there are pyhton libraries you want to take advantage of in Spark.
# # Estimating $\pi$
#
# This PySpark example shows you how to estimate $\pi$ in parallel
# using Monte Carlo integration.
from __future__ import print_function
import sys
from random import random
from operator import add
partitions = 2
n = 100000 * partitions
def f(_):
x = random() * 2 - 1
y = random() * 2 - 1
return 1 if x ** 2 + y ** 2 < 1 else 0
# To access the associated SparkContext
count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
print("Pi is roughly %f" % (4.0 * count / n))
Pi is roughly 3.138820
The following is from this google turotial.
Programming a Monte Carlo simulation in Scala
Monte Carlo, of course, is famous as a gambling destination. In this section, you use Scala to create a simulation that models the mathematical advantage that a casino enjoys in a game of chance. The "house edge" at a real casino varies widely from game to game; it can be over 20% in keno, for example. This tutorial creates a simple game where the house has only a one-percent advantage. Here's how the game works:
- The player places a bet, consisting of a number of chips from a bankroll fund.
- The player rolls a 100-sided die (how cool would that be?).
- If the result of the roll is a number from 1 to 49, the player wins.
- For results 50 to 100, the player loses the bet.
You can see that this game creates a one-percent disadvantage for the player: in 51 of the 100 possible outcomes for each roll, the player loses.
Follow these steps to create and run the game:
val STARTING_FUND = 10
val STAKE = 1 // the amount of the bet
val NUMBER_OF_GAMES = 25
def rollDie: Int = {
val r = scala.util.Random
r.nextInt(99) + 1
}
def playGame(stake: Int): (Int) = {
val faceValue = rollDie
if (faceValue < 50)
(2*stake)
else
(0)
}
// Function to play the game multiple times
// Returns the final fund amount
def playSession(
startingFund: Int = STARTING_FUND,
stake: Int = STAKE,
numberOfGames: Int = NUMBER_OF_GAMES):
(Int) = {
// Initialize values
var (currentFund, currentStake, currentGame) = (startingFund, 0, 1)
// Keep playing until number of games is reached or funds run out
while (currentGame <= numberOfGames && currentFund > 0) {
// Set the current bet and deduct it from the fund
currentStake = math.min(stake, currentFund)
currentFund -= currentStake
// Play the game
val (winnings) = playGame(currentStake)
// Add any winnings
currentFund += winnings
// Increment the loop counter
currentGame += 1
}
(currentFund)
}
STARTING_FUND: Int = 10
STAKE: Int = 1
NUMBER_OF_GAMES: Int = 25
rollDie: Int
playGame: (stake: Int)Int
playSession: (startingFund: Int, stake: Int, numberOfGames: Int)Int
Enter the following code to play the game 25 times, which is the default value for NUMBER_OF_GAMES.
playSession()
res31: Int = 5
Your bankroll started with a value of 10 units. Is it higher or lower, now?
Now simulate 10,000 players betting 100 chips per game. Play 10,000 games in a session. This Monte Carlo simulation calculates the probability of losing all your money before the end of the session. Enter the follow code:
(sc.parallelize(1 to 10000, 500)
.map(i => playSession(100000, 100, 250000))
.map(i => if (i == 0) 1 else 0)
.reduce(_+_)/10000.0)
res32: Double = 0.9992
Note that the syntax .reduce(_+_) is shorthand in Scala for aggregating by using a summing function.
The preceding code performs the following steps:
- Creates an RDD with the results of playing the session.
- Replaces bankrupt players' results with the number 1 and nonzero results with the number 0.
- Sums the count of bankrupt players.
- Divides the count by the number of players.
A typical result might be:
res32: Double = 0.9992
Which represents a near guarantee of losing all your money, even though the casino had only a one-percent advantage.
Project Ideas
Try to create a scalable simulation of interest to you.
Here are some mature projects:
- https://github.com/zishanfu/GeoSparkSim
- https://github.com/srbaird/mc-var-spark
See a more complete VaR modeling: - https://databricks.com/blog/2020/05/27/modernizing-risk-management-part-1-streaming-data-ingestion-rapid-model-development-and-monte-carlo-simulations-at-scale.html
Introduction to Machine Learning
Some very useful resources we will weave around for Statistical Learning, Data Mining, Machine Learning:
- January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR).
- https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
- free PDF of the ISLR book: http://www-bcf.usc.edu/~gareth/ISL/
- A more theoretically sound book with interesting aplications is Elements of Statistical Learning by the Stanford Gang of 3 (Hastie, Tibshirani and Friedman):
- free PDF of the 10th printing: http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
- Solutions: http://waxworksmath.com/Authors/GM/Hastie/WriteUp/weatherwaxepsteinhastiesolutions_manual.pdf
- A great series on Probabilistic ML by Kevin P. Murphy https://probml.github.io/pml-book/.
Deep Learning is a very popular method currently (2017) in Machine Learning and I recommend Andrew Ng's free course in Courseera for this if you have not taken a course in deep learning already.
Note: This is an applied course in data science and we will quickly move to doing things with data. You have to do work to get a deeper mathematical understanding or take other courses. We will focus on intution here and the distributed ML Pipeline in action.
I am assuming several of you have or are taking ML courses. In this course we will focus on getting our hads dirty quickly with some level of common understanding across all the disciplines represented here. Please dive deep at your own time to desired depths and tangents based on your background.
Summary of Machine Learning at a High Level
- A rough definition of machine learning.
- constructing and studying algorithms that learn from and make predictions on data.
- This broad area involves tools and ideas from various domains, including:
- computer science,
- probability and statistics,
- optimization,
- linear algebra
- logic
- etc.
- Common examples of ML, include:
- facial recognition,
- link prediction,
- text or document classification, eg.::
- spam detection,
- protein structure prediction
- teaching computers to play games (go!)
Some common terminology
using example of spam detection as a running example.
-
the data points we learn from are call observations:
- they are items or entities used for::
- learning or
- evaluation.
- they are items or entities used for::
-
in the context of spam detection,
- emails are our observations.
- Features are attributes used to represent an observation.
- Features are typically numeric,
- and in spam detection, they can be:
- the length,
- the date, or
- the presence or absence of keywords in emails.
- Labels are values or categories assigned to observations.
- and in spam detection, they can be:
- an email being defined as spam or not spam.
-
Training and test data sets are the observations that we use to train and evaluate a learning algorithm.
-
Pop-Quiz
- What is the difference between supervised and unsupervised learning?
For a Stats@Stanford Hastie-Tibshirani Perspective on Supervised and Unsupervised Learning:
(watch later 12:12):
From : https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/.
ML Pipelines
Here we will use ML Pipelines to do machine learning at scale.
See https://spark.apache.org/docs/latest/ml-pipeline.html for a quick overview.
Read this section for an overview:
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
When you first hear a song, do you ever categorize it as slow or fast in your head? Is it even a valid categorization? If so, can one do it automatically? I have always wondered about that. That is why I got excited when I learned about the Million Songs Dataset -Kaggle Challenge.
In this tutorial we will walk through a practical example of a data science project with Apache Spark in Python. We are going to parse, explore and model a sample from the million songs dataset stored on distributed storage. This tutorial is organized into three sections:
- ETL: Parses raw texts and creates a cached table
- Explore: Explores different aspects of the songs table using graphs
- Model: Uses SparkML to cluster songs based on some of their attributes

The goal of this tutorial is to prepare you for real world data science projects. Make sure you go through the tutorial in the above order and use the exercises to make yourself familiar further with the API. Also make sure you run these notebooks on a 1.6.x cluster.
1. ETL
The first step of most data science projects is extracting, transforming and loading data into well formated tables. Our example starts with ETL as well. By following the ETL noteboook you can expect to learn about following Spark concepts:
- RDD: Resilient Distributed Dataset
- Reading and transforming RDDs
- Schema in Spark
- Spark DataFrame
- Temp tables
- Caching tables
2. Explore
Exploratory analysis is a key step in any real data project. Data scientists use variety of tools to explore and visualize their data. In the second notebook of this tutorial we introduce several tools in Python and Databricks notebooks that can help you visually explore your large data. By reading this notebook and finishing its exercises you will become familiar with:
- How to view the schema of a table
- How to display ggplot and matplotlib figures in Notebooks
- How to summarize and visualize different aspects of large datasets
- How to sample and visualize large data
3. Model
The end goal of many data scientists is producing useful models. These models are often used for prediction of new and upcoming events in production. In our third notebook we construct a simple K-means clustering model. In this notebook you will learn about:
- Feature transformation
- Fitting a model using SparkML API
- Applying a model to data
- Visualizing model results
- Model tuning
013_UnsupervisedClustering_1MSongsKMeans_Stage1ETL
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
Stage 2: Exploring songs data

This is the second notebook in this tutorial. In this notebook we do what any data scientist does with their data right after parsing it: exploring and understanding different aspect of data. Make sure you understand how we get the songsTable by reading and running the ETL notebook. In the ETL notebook we created and cached a temporary table named songsTable
// Let's quickly do everything to register the tempView of the table here
// fill in comment ... EXERCISE!
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
def parseLine(line: String): Song = {
// fill in comment ...
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
// fill in comment ...
val tokens = line.split("\t")
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/datasets/sds/songs/data-001/part-*")
// .. fill in comment
val df = dataRDD.map(parseLine).toDF
// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[81] at textFile at command-2971213210276755:30
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
spark.catalog.listTables.show(false) // make sure the temp view of our table is there
+----------------------------+--------+-----------+---------+-----------+
|name |database|description|tableType|isTemporary|
+----------------------------+--------+-----------+---------+-----------+
|countrycodes |default |null |EXTERNAL |false |
|ltcar_locations_2_csv |default |null |MANAGED |false |
|magellan |default |null |MANAGED |false |
|mobile_sample |default |null |EXTERNAL |false |
|over300all_2_txt |default |null |MANAGED |false |
|person |default |null |MANAGED |false |
|persons |default |null |MANAGED |false |
|social_media_usage |default |null |MANAGED |false |
|social_media_usage_csv_gui |default |null |MANAGED |false |
|voronoi20191213uppsla1st_txt|default |null |MANAGED |false |
|voronoi20191213uppsla2d_txt |default |null |MANAGED |false |
|voronoi20191213uppsla3d_txt |default |null |MANAGED |false |
|songstable |null |null |TEMPORARY|true |
+----------------------------+--------+-----------+---------+-----------+
A first inspection
A first step to any data exploration is viewing sample data. For this purpose we can use a simple SQL query that returns first 10 rows.
select * from songsTable limit 10
| artist_id | artist_latitude | artist_longitude | artist_location | artist_name | duration | end_of_fade_in | key | key_confidence | loudness | release | song_hotness | song_id | start_of_fade_out | tempo | time_signature | time_signature_confidence | title | year | partial_sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AR81V6H1187FB48872 | 0.0 | 0.0 | Earl Sixteen | 213.7073 | 0.0 | 11.0 | 0.419 | -12.106 | Soldier of Jah Army | 0.0 | SOVNZSZ12AB018A9B8 | 208.289 | 125.882 | 1.0 | 0.0 | Rastaman | 2003.0 | -1.0 | |
| ARVVZQP11E2835DBCB | 0.0 | 0.0 | Wavves | 133.25016 | 0.0 | 0.0 | 0.282 | 0.596 | Wavvves | 0.471578247701 | SOJTQHQ12A8C143C5F | 128.116 | 89.519 | 1.0 | 0.0 | I Want To See You (And Go To The Movies) | 2009.0 | -1.0 | |
| ARFG9M11187FB3BBCB | 0.0 | 0.0 | Nashua USA | C-Side | 247.32689 | 0.0 | 9.0 | 0.612 | -4.896 | Santa Festival Compilation 2008 vol.1 | 0.0 | SOAJSQL12AB0180501 | 242.196 | 171.278 | 5.0 | 1.0 | Loose on the Dancefloor | 0.0 | 225261.0 |
| ARK4Z2O1187FB45FF0 | 0.0 | 0.0 | Harvest | 337.05751 | 0.247 | 4.0 | 0.46 | -9.092 | Underground Community | 0.0 | SOTDRVW12AB018BEB9 | 327.436 | 84.986 | 4.0 | 0.673 | No Return | 0.0 | 101619.0 | |
| AR4VQSG1187FB57E18 | 35.25082 | -91.74015 | Searcy, AR | Gossip | 430.23628 | 0.0 | 2.0 | 3.4e-2 | -6.846 | Yr Mangled Heart | 0.0 | SOTVOCL12A8AE478DD | 424.06 | 121.998 | 4.0 | 0.847 | Yr Mangled Heart | 2006.0 | 740623.0 |
| ARNBV1X1187B996249 | 0.0 | 0.0 | Alex | 186.80118 | 0.0 | 4.0 | 0.641 | -16.108 | Jolgaledin | 0.0 | SODTGRY12AB0182438 | 166.156 | 140.735 | 4.0 | 5.5e-2 | Mariu Sonur Jesus | 0.0 | 673970.0 | |
| ARXOEZX1187B9B82A1 | 0.0 | 0.0 | Elie Attieh | 361.89995 | 0.0 | 7.0 | 0.863 | -4.919 | ELITE | 0.0 | SOIINTJ12AB0180BA6 | 354.476 | 128.024 | 4.0 | 0.399 | Fe Yom We Leila | 0.0 | 280304.0 | |
| ARXPUIA1187B9A32F1 | 0.0 | 0.0 | Rome, Italy | Simone Cristicchi | 220.00281 | 2.119 | 4.0 | 0.486 | -6.52 | Dall'Altra Parte Del Cancello | 0.484225272411 | SONHXJK12AAF3B5290 | 214.761 | 99.954 | 1.0 | 0.928 | L'Italiano | 2007.0 | 745962.0 |
| ARNPPTH1187B9AD429 | 51.4855 | -0.37196 | Heston, Middlesex, England | Jimmy Page | 156.86485 | 0.334 | 7.0 | 0.493 | -9.962 | No Introduction Necessary [Deluxe Edition] | 0.0 | SOGUHGW12A58A80E06 | 149.269 | 162.48 | 4.0 | 0.534 | Wailing Sounds | 2004.0 | 599250.0 |
| AROGWRA122988FEE45 | 0.0 | 0.0 | Christos Dantis | 256.67873 | 2.537 | 9.0 | 0.742 | -13.404 | Daktilika Apotipomata | 0.0 | SOJJOYI12A8C13399D | 248.912 | 134.944 | 4.0 | 0.162 | Stin Proigoumeni Zoi | 0.0 | 611396.0 |
table("songsTable").printSchema()
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
select count(*) from songsTable
| count(1) |
|---|
| 31369.0 |
table("songsTable").count() // or equivalently with DataFrame API - recall table("songsTable") is a DataFrame
res4: Long = 31369
display(sqlContext.sql("SELECT duration, year FROM songsTable")) // Aggregation is set to 'Average' in 'Plot Options'
| duration | year |
|---|---|
| 213.7073 | 2003.0 |
| 133.25016 | 2009.0 |
| 247.32689 | 0.0 |
| 337.05751 | 0.0 |
| 430.23628 | 2006.0 |
| 186.80118 | 0.0 |
| 361.89995 | 0.0 |
| 220.00281 | 2007.0 |
| 156.86485 | 2004.0 |
| 256.67873 | 0.0 |
| 204.64281 | 0.0 |
| 112.48281 | 0.0 |
| 170.39628 | 0.0 |
| 215.95383 | 0.0 |
| 303.62077 | 0.0 |
| 266.60526 | 0.0 |
| 326.19057 | 1997.0 |
| 51.04281 | 2009.0 |
| 129.4624 | 0.0 |
| 253.33506 | 2003.0 |
| 237.76608 | 2004.0 |
| 132.96281 | 0.0 |
| 399.35955 | 2006.0 |
| 168.75057 | 1991.0 |
| 396.042 | 0.0 |
| 192.10404 | 1968.0 |
| 12.2771 | 2006.0 |
| 367.56853 | 0.0 |
| 189.93587 | 0.0 |
| 233.50812 | 0.0 |
| 462.68036 | 0.0 |
| 202.60526 | 0.0 |
| 241.52771 | 0.0 |
| 275.64363 | 1992.0 |
| 350.69342 | 2007.0 |
| 166.55628 | 1968.0 |
| 249.49506 | 1983.0 |
| 53.86404 | 1992.0 |
| 233.76934 | 2001.0 |
| 275.12118 | 2009.0 |
| 191.13751 | 2006.0 |
| 299.07546 | 0.0 |
| 468.74077 | 0.0 |
| 110.34077 | 0.0 |
| 234.78812 | 2003.0 |
| 705.25342 | 2006.0 |
| 383.52934 | 0.0 |
| 196.10077 | 0.0 |
| 299.20608 | 1998.0 |
| 94.04036 | 0.0 |
| 28.08118 | 2006.0 |
| 207.93424 | 2006.0 |
| 152.0322 | 1999.0 |
| 207.96036 | 2002.0 |
| 371.25179 | 0.0 |
| 288.93995 | 2002.0 |
| 235.93751 | 2004.0 |
| 505.70404 | 0.0 |
| 177.57995 | 0.0 |
| 376.842 | 2004.0 |
| 266.84036 | 2004.0 |
| 270.8371 | 2006.0 |
| 178.18077 | 0.0 |
| 527.17669 | 0.0 |
| 244.27057 | 0.0 |
| 436.47955 | 2006.0 |
| 236.79955 | 0.0 |
| 134.53016 | 2005.0 |
| 181.002 | 0.0 |
| 239.41179 | 1999.0 |
| 72.98567 | 0.0 |
| 214.36036 | 2001.0 |
| 150.59546 | 2007.0 |
| 152.45016 | 1970.0 |
| 218.17424 | 0.0 |
| 290.63791 | 0.0 |
| 149.05424 | 0.0 |
| 440.21506 | 0.0 |
| 212.34893 | 1988.0 |
| 278.67383 | 0.0 |
| 269.60934 | 1974.0 |
| 182.69995 | 2002.0 |
| 207.882 | 2007.0 |
| 102.50404 | 0.0 |
| 437.60281 | 0.0 |
| 216.11057 | 2009.0 |
| 193.25342 | 0.0 |
| 234.16118 | 2009.0 |
| 695.77098 | 0.0 |
| 297.58649 | 1996.0 |
| 265.37751 | 2000.0 |
| 182.85669 | 1990.0 |
| 202.23955 | 0.0 |
| 390.08608 | 2009.0 |
| 242.78159 | 2000.0 |
| 242.54649 | 2002.0 |
| 496.66567 | 2004.0 |
| 395.36281 | 0.0 |
| 234.89261 | 1999.0 |
| 237.84444 | 2005.0 |
| 313.57342 | 2009.0 |
| 489.22077 | 2001.0 |
| 239.98649 | 2004.0 |
| 128.65261 | 0.0 |
| 193.07057 | 0.0 |
| 144.19546 | 0.0 |
| 196.96281 | 2006.0 |
| 222.06649 | 1997.0 |
| 58.38322 | 0.0 |
| 346.14812 | 1998.0 |
| 406.54322 | 0.0 |
| 304.09098 | 2009.0 |
| 180.21832 | 2003.0 |
| 213.41995 | 0.0 |
| 323.44771 | 0.0 |
| 54.7522 | 2009.0 |
| 437.02812 | 1994.0 |
| 268.7473 | 2009.0 |
| 104.75057 | 0.0 |
| 248.60689 | 2006.0 |
| 221.41342 | 0.0 |
| 237.81832 | 1991.0 |
| 216.34567 | 2009.0 |
| 78.94159 | 0.0 |
| 47.22893 | 2005.0 |
| 202.00444 | 2007.0 |
| 293.56363 | 0.0 |
| 206.44526 | 1986.0 |
| 267.78077 | 2003.0 |
| 187.27138 | 2008.0 |
| 249.05098 | 2009.0 |
| 221.51791 | 0.0 |
| 452.88444 | 0.0 |
| 163.76118 | 1992.0 |
| 257.17506 | 0.0 |
| 235.78077 | 0.0 |
| 257.82812 | 1996.0 |
| 195.34322 | 0.0 |
| 478.1971 | 0.0 |
| 268.01587 | 1997.0 |
| 136.93342 | 1983.0 |
| 397.53098 | 0.0 |
| 194.69016 | 2001.0 |
| 580.80608 | 0.0 |
| 177.71057 | 2006.0 |
| 257.43628 | 1999.0 |
| 184.13669 | 0.0 |
| 64.57424 | 2001.0 |
| 123.92444 | 1993.0 |
| 257.07057 | 0.0 |
| 219.48036 | 1996.0 |
| 679.41832 | 0.0 |
| 252.29016 | 1995.0 |
| 311.90159 | 2004.0 |
| 252.76036 | 1998.0 |
| 138.94485 | 0.0 |
| 428.64281 | 0.0 |
| 295.31383 | 0.0 |
| 212.03546 | 0.0 |
| 426.50077 | 0.0 |
| 197.11955 | 0.0 |
| 191.55546 | 0.0 |
| 187.53261 | 2006.0 |
| 184.97261 | 2004.0 |
| 388.41424 | 2009.0 |
| 218.90567 | 0.0 |
| 246.49098 | 0.0 |
| 452.88444 | 0.0 |
| 223.18975 | 0.0 |
| 245.2371 | 0.0 |
| 148.92363 | 0.0 |
| 362.81424 | 2005.0 |
| 171.44118 | 0.0 |
| 207.72526 | 2005.0 |
| 191.29424 | 0.0 |
| 208.50893 | 0.0 |
| 240.24771 | 1995.0 |
| 373.44608 | 2002.0 |
| 172.01587 | 0.0 |
| 153.25995 | 2007.0 |
| 242.36363 | 1994.0 |
| 177.55383 | 0.0 |
| 263.20934 | 1994.0 |
| 191.03302 | 2007.0 |
| 232.77669 | 0.0 |
| 220.65587 | 0.0 |
| 132.57098 | 2002.0 |
| 189.6224 | 1993.0 |
| 32.522 | 1997.0 |
| 173.94893 | 0.0 |
| 268.01587 | 2006.0 |
| 91.97669 | 0.0 |
| 215.77098 | 0.0 |
| 195.47383 | 0.0 |
| 234.81424 | 1977.0 |
| 110.78485 | 0.0 |
| 155.74159 | 0.0 |
| 172.5122 | 0.0 |
| 227.76118 | 1995.0 |
| 233.01179 | 2007.0 |
| 298.89261 | 0.0 |
| 245.36771 | 1994.0 |
| 276.08771 | 2005.0 |
| 375.77098 | 2003.0 |
| 273.71057 | 0.0 |
| 226.92526 | 0.0 |
| 196.46649 | 0.0 |
| 199.65342 | 1995.0 |
| 243.40853 | 0.0 |
| 207.62077 | 2006.0 |
| 252.73424 | 0.0 |
| 244.32281 | 0.0 |
| 152.65914 | 0.0 |
| 203.88526 | 2003.0 |
| 120.16281 | 0.0 |
| 214.77832 | 1977.0 |
| 204.9824 | 0.0 |
| 118.30812 | 1996.0 |
| 205.26975 | 0.0 |
| 499.22567 | 0.0 |
| 217.83465 | 2005.0 |
| 192.57424 | 2005.0 |
| 328.09751 | 0.0 |
| 298.03057 | 1968.0 |
| 501.49832 | 0.0 |
| 276.40118 | 0.0 |
| 507.55873 | 2006.0 |
| 191.08526 | 2008.0 |
| 324.38812 | 0.0 |
| 218.56608 | 0.0 |
| 232.30649 | 0.0 |
| 295.05261 | 1972.0 |
| 225.74975 | 2003.0 |
| 522.00444 | 0.0 |
| 245.86404 | 1967.0 |
| 263.67955 | 0.0 |
| 556.61669 | 2009.0 |
| 227.94404 | 1998.0 |
| 83.82649 | 1964.0 |
| 242.85995 | 0.0 |
| 233.09016 | 2008.0 |
| 201.74322 | 0.0 |
| 476.15955 | 0.0 |
| 370.93832 | 2005.0 |
| 229.17179 | 0.0 |
| 288.07791 | 2001.0 |
| 91.34975 | 0.0 |
| 230.79138 | 2005.0 |
| 256.46975 | 2003.0 |
| 203.44118 | 0.0 |
| 230.81751 | 2003.0 |
| 272.29995 | 0.0 |
| 201.22077 | 2008.0 |
| 204.93016 | 2010.0 |
| 372.84526 | 0.0 |
| 63.65995 | 2005.0 |
| 412.15955 | 0.0 |
| 270.10567 | 0.0 |
| 104.6722 | 0.0 |
| 214.25587 | 1970.0 |
| 230.05995 | 0.0 |
| 155.74159 | 0.0 |
| 218.04363 | 2008.0 |
| 357.77261 | 2007.0 |
| 318.27546 | 1985.0 |
| 444.55138 | 2010.0 |
| 509.07383 | 0.0 |
| 176.95302 | 0.0 |
| 95.34649 | 0.0 |
| 207.67302 | 0.0 |
| 256.67873 | 1994.0 |
| 252.78649 | 0.0 |
| 234.60526 | 0.0 |
| 167.65342 | 0.0 |
| 266.16118 | 0.0 |
| 188.05506 | 0.0 |
| 229.14567 | 2009.0 |
| 227.00363 | 2004.0 |
| 74.50077 | 1992.0 |
| 222.09261 | 0.0 |
| 212.68853 | 1984.0 |
| 155.74159 | 0.0 |
| 153.65179 | 0.0 |
| 548.51873 | 0.0 |
| 445.90975 | 2003.0 |
| 317.49179 | 1999.0 |
| 140.32934 | 0.0 |
| 309.4722 | 0.0 |
| 142.91546 | 0.0 |
| 429.24363 | 2007.0 |
| 172.19873 | 0.0 |
| 215.562 | 0.0 |
| 290.79465 | 2009.0 |
| 197.04118 | 0.0 |
| 309.44608 | 0.0 |
| 265.01179 | 1999.0 |
| 257.64526 | 2000.0 |
| 203.54567 | 0.0 |
| 161.56689 | 0.0 |
| 177.84118 | 0.0 |
| 260.04853 | 2004.0 |
| 195.00363 | 1988.0 |
| 268.042 | 0.0 |
| 195.97016 | 1991.0 |
| 351.92118 | 0.0 |
| 119.35302 | 0.0 |
| 177.24036 | 0.0 |
| 259.83955 | 0.0 |
| 222.51057 | 2008.0 |
| 163.97016 | 2004.0 |
| 139.49342 | 0.0 |
| 158.77179 | 0.0 |
| 193.4624 | 2000.0 |
| 131.082 | 1963.0 |
| 190.95465 | 1998.0 |
| 413.3873 | 2005.0 |
| 134.73914 | 1966.0 |
| 162.40281 | 1965.0 |
| 243.59138 | 1965.0 |
| 180.84526 | 0.0 |
| 315.14077 | 0.0 |
| 221.51791 | 1994.0 |
| 122.53995 | 2008.0 |
| 243.43465 | 1990.0 |
| 200.202 | 1982.0 |
| 95.50322 | 2000.0 |
| 200.4371 | 1998.0 |
| 186.93179 | 0.0 |
| 492.22485 | 1999.0 |
| 359.33995 | 1972.0 |
| 89.39057 | 1990.0 |
| 212.81914 | 0.0 |
| 315.03628 | 1996.0 |
| 214.69995 | 0.0 |
| 137.92608 | 1993.0 |
| 559.49016 | 0.0 |
| 382.14485 | 1991.0 |
| 430.31465 | 2008.0 |
| 171.25832 | 0.0 |
| 210.12853 | 2002.0 |
| 53.18485 | 2005.0 |
| 78.65424 | 1993.0 |
| 209.162 | 2008.0 |
| 237.60934 | 2006.0 |
| 184.47628 | 2009.0 |
| 323.02975 | 1997.0 |
| 158.27546 | 0.0 |
| 213.86404 | 0.0 |
| 470.69995 | 0.0 |
| 229.79873 | 2005.0 |
| 392.22812 | 0.0 |
| 196.62322 | 0.0 |
| 80.97914 | 0.0 |
| 124.55138 | 1989.0 |
| 230.32118 | 1971.0 |
| 132.51873 | 0.0 |
| 112.95302 | 1994.0 |
| 131.52608 | 0.0 |
| 153.25995 | 2010.0 |
| 211.01669 | 0.0 |
| 218.93179 | 2008.0 |
| 175.0722 | 2010.0 |
| 116.61016 | 1997.0 |
| 251.45424 | 2001.0 |
| 269.50485 | 2004.0 |
| 231.47057 | 0.0 |
| 298.37016 | 1996.0 |
| 314.122 | 2005.0 |
| 263.99302 | 0.0 |
| 480.91383 | 2001.0 |
| 305.10975 | 0.0 |
| 280.16281 | 0.0 |
| 295.65342 | 1999.0 |
| 411.45424 | 2007.0 |
| 265.97832 | 0.0 |
| 153.96526 | 0.0 |
| 210.31138 | 1970.0 |
| 241.44934 | 0.0 |
| 235.33669 | 0.0 |
| 352.65261 | 0.0 |
| 293.35465 | 0.0 |
| 243.66975 | 2003.0 |
| 133.22404 | 0.0 |
| 233.03791 | 0.0 |
| 339.93098 | 0.0 |
| 249.80853 | 1993.0 |
| 253.72689 | 2004.0 |
| 94.35383 | 1981.0 |
| 130.63791 | 0.0 |
| 195.36934 | 0.0 |
| 229.25016 | 2007.0 |
| 314.64444 | 2007.0 |
| 329.1424 | 1998.0 |
| 224.46975 | 1990.0 |
| 215.562 | 1987.0 |
| 236.85179 | 1990.0 |
| 197.11955 | 1957.0 |
| 251.76771 | 2004.0 |
| 183.50975 | 0.0 |
| 268.01587 | 2005.0 |
| 413.02159 | 0.0 |
| 385.17506 | 2000.0 |
| 358.16444 | 0.0 |
| 164.77995 | 0.0 |
| 253.36118 | 2004.0 |
| 196.49261 | 2007.0 |
| 157.6224 | 1999.0 |
| 310.93506 | 0.0 |
| 434.96444 | 1991.0 |
| 157.04771 | 1991.0 |
| 266.16118 | 2007.0 |
| 267.59791 | 1977.0 |
| 303.90812 | 0.0 |
| 277.18485 | 2009.0 |
| 272.22159 | 0.0 |
| 155.95057 | 0.0 |
| 127.00689 | 1997.0 |
| 152.86812 | 2005.0 |
| 224.7571 | 1990.0 |
| 175.41179 | 0.0 |
| 151.97995 | 0.0 |
| 199.99302 | 0.0 |
| 251.53261 | 0.0 |
| 252.96934 | 2004.0 |
| 181.13261 | 1984.0 |
| 195.49995 | 0.0 |
| 328.202 | 2001.0 |
| 187.71546 | 0.0 |
| 166.94812 | 1985.0 |
| 242.72934 | 1988.0 |
| 218.80118 | 2005.0 |
| 205.68771 | 0.0 |
| 146.93832 | 1996.0 |
| 449.4624 | 2000.0 |
| 503.40526 | 0.0 |
| 181.34159 | 0.0 |
| 143.90812 | 0.0 |
| 406.36036 | 0.0 |
| 269.87057 | 0.0 |
| 265.29914 | 0.0 |
| 242.88608 | 0.0 |
| 110.39302 | 0.0 |
| 262.84363 | 0.0 |
| 334.00118 | 1990.0 |
| 173.81832 | 2007.0 |
| 608.78322 | 0.0 |
| 197.22404 | 0.0 |
| 163.94404 | 2008.0 |
| 93.09995 | 2001.0 |
| 206.75873 | 0.0 |
| 183.50975 | 0.0 |
| 402.442 | 0.0 |
| 735.79057 | 1986.0 |
| 233.19465 | 1997.0 |
| 326.55628 | 0.0 |
| 525.50485 | 0.0 |
| 396.19873 | 0.0 |
| 171.12771 | 0.0 |
| 318.1971 | 2006.0 |
| 323.70893 | 2002.0 |
| 526.99383 | 0.0 |
| 161.09669 | 1991.0 |
| 168.41098 | 1990.0 |
| 249.57342 | 0.0 |
| 405.4722 | 0.0 |
| 271.0722 | 2010.0 |
| 190.69342 | 2009.0 |
| 151.61424 | 2001.0 |
| 121.57342 | 0.0 |
| 117.08036 | 0.0 |
| 244.24444 | 2008.0 |
| 246.85669 | 0.0 |
| 144.03873 | 2007.0 |
| 169.79546 | 1988.0 |
| 193.93261 | 2004.0 |
| 325.77261 | 0.0 |
| 337.34485 | 0.0 |
| 143.67302 | 2009.0 |
| 211.69587 | 0.0 |
| 299.4673 | 1978.0 |
| 159.76444 | 0.0 |
| 337.31873 | 0.0 |
| 259.18649 | 2007.0 |
| 221.64853 | 0.0 |
| 164.54485 | 0.0 |
| 56.34567 | 0.0 |
| 184.21506 | 0.0 |
| 249.23383 | 2010.0 |
| 127.29424 | 1994.0 |
| 306.6771 | 1980.0 |
| 168.98567 | 0.0 |
| 290.2722 | 0.0 |
| 182.33424 | 2004.0 |
| 180.92363 | 0.0 |
| 233.76934 | 1990.0 |
| 423.70567 | 0.0 |
| 139.36281 | 0.0 |
| 289.72363 | 2005.0 |
| 100.96281 | 2005.0 |
| 153.05098 | 2009.0 |
| 129.25342 | 0.0 |
| 190.11873 | 1993.0 |
| 158.1971 | 0.0 |
| 234.94485 | 2000.0 |
| 256.02567 | 0.0 |
| 279.84934 | 0.0 |
| 217.7824 | 2005.0 |
| 271.62077 | 2005.0 |
| 372.34893 | 0.0 |
| 264.88118 | 0.0 |
| 270.18404 | 1984.0 |
| 42.86649 | 0.0 |
| 247.27465 | 0.0 |
| 185.10322 | 1990.0 |
| 333.94893 | 0.0 |
| 380.49914 | 1999.0 |
| 517.72036 | 0.0 |
| 208.95302 | 2006.0 |
| 359.73179 | 0.0 |
| 378.72281 | 1995.0 |
| 110.41914 | 0.0 |
| 237.37424 | 2003.0 |
| 136.30649 | 0.0 |
| 153.73016 | 2005.0 |
| 209.8673 | 2007.0 |
| 224.86159 | 0.0 |
| 202.34404 | 0.0 |
| 229.43302 | 0.0 |
| 300.56444 | 2003.0 |
| 264.35873 | 0.0 |
| 213.9424 | 0.0 |
| 164.77995 | 2004.0 |
| 206.75873 | 0.0 |
| 249.73016 | 2009.0 |
| 521.11628 | 2002.0 |
| 240.09098 | 0.0 |
| 347.89832 | 0.0 |
| 224.96608 | 1993.0 |
| 250.25261 | 0.0 |
| 419.00363 | 0.0 |
| 593.3971 | 1958.0 |
| 269.89669 | 1999.0 |
| 235.12771 | 2009.0 |
| 180.76689 | 0.0 |
| 304.03873 | 2004.0 |
| 253.36118 | 2006.0 |
| 311.74485 | 2006.0 |
| 353.43628 | 0.0 |
| 337.00526 | 0.0 |
| 305.00526 | 2006.0 |
| 113.76281 | 2007.0 |
| 379.74159 | 0.0 |
| 258.76853 | 1993.0 |
| 157.64853 | 0.0 |
| 352.28689 | 0.0 |
| 221.51791 | 0.0 |
| 249.44281 | 0.0 |
| 205.42649 | 0.0 |
| 166.922 | 0.0 |
| 250.25261 | 0.0 |
| 224.73098 | 2003.0 |
| 316.83873 | 2002.0 |
| 269.34812 | 2007.0 |
| 188.02893 | 0.0 |
| 276.87138 | 2001.0 |
| 263.02649 | 0.0 |
| 320.44363 | 0.0 |
| 531.43465 | 2005.0 |
| 126.85016 | 2008.0 |
| 232.01914 | 0.0 |
| 243.87873 | 0.0 |
| 288.60036 | 2004.0 |
| 817.57995 | 2007.0 |
| 200.9073 | 0.0 |
| 229.48526 | 2009.0 |
| 263.65342 | 1971.0 |
| 209.71057 | 2008.0 |
| 430.54975 | 2007.0 |
| 531.9571 | 0.0 |
| 277.39383 | 0.0 |
| 253.41342 | 1999.0 |
| 538.5922 | 0.0 |
| 187.34975 | 0.0 |
| 189.67465 | 2006.0 |
| 247.66649 | 0.0 |
| 196.15302 | 2008.0 |
| 248.45016 | 0.0 |
| 266.26567 | 2005.0 |
| 174.41914 | 0.0 |
| 241.21424 | 1996.0 |
| 213.39383 | 0.0 |
| 201.66485 | 1956.0 |
| 141.16526 | 0.0 |
| 198.76526 | 2010.0 |
| 234.03057 | 2002.0 |
| 293.77261 | 0.0 |
| 149.83791 | 0.0 |
| 193.09669 | 0.0 |
| 416.62649 | 2007.0 |
| 206.18404 | 2008.0 |
| 292.15302 | 0.0 |
| 209.55383 | 1997.0 |
| 303.46404 | 0.0 |
| 284.31628 | 0.0 |
| 209.34485 | 0.0 |
| 131.34322 | 2010.0 |
| 127.16363 | 0.0 |
| 228.98893 | 1983.0 |
| 18.18077 | 0.0 |
| 202.762 | 1999.0 |
| 475.21914 | 1989.0 |
| 434.52036 | 2002.0 |
| 306.36363 | 0.0 |
| 251.84608 | 2007.0 |
| 392.80281 | 1999.0 |
| 191.63383 | 0.0 |
| 207.90812 | 0.0 |
| 298.86649 | 0.0 |
| 195.36934 | 0.0 |
| 236.06812 | 1995.0 |
| 315.76771 | 2009.0 |
| 214.5171 | 0.0 |
| 140.90404 | 0.0 |
| 147.66975 | 0.0 |
| 230.50404 | 0.0 |
| 259.99628 | 2010.0 |
| 234.70975 | 1994.0 |
| 191.97342 | 1992.0 |
| 305.6322 | 0.0 |
| 197.53751 | 1997.0 |
| 152.05832 | 0.0 |
| 360.82893 | 1998.0 |
| 440.37179 | 0.0 |
| 211.09506 | 2009.0 |
| 362.60526 | 1998.0 |
| 364.64281 | 1997.0 |
| 267.12771 | 0.0 |
| 380.81261 | 2007.0 |
| 248.13669 | 1995.0 |
| 253.20444 | 0.0 |
| 244.03546 | 0.0 |
| 159.13751 | 0.0 |
| 246.12526 | 0.0 |
| 40.95955 | 2005.0 |
| 200.04526 | 2007.0 |
| 155.08853 | 0.0 |
| 144.66567 | 0.0 |
| 170.86649 | 0.0 |
| 286.71955 | 2001.0 |
| 333.19138 | 1996.0 |
| 542.1971 | 0.0 |
| 222.37995 | 0.0 |
| 195.68281 | 2003.0 |
| 440.00608 | 0.0 |
| 223.08526 | 0.0 |
| 378.98404 | 0.0 |
| 91.45424 | 1983.0 |
| 114.65098 | 2009.0 |
| 218.80118 | 0.0 |
| 242.36363 | 0.0 |
| 143.0722 | 1962.0 |
| 242.78159 | 2007.0 |
| 256.31302 | 0.0 |
| 244.37506 | 0.0 |
| 36.54485 | 2007.0 |
| 401.94567 | 1999.0 |
| 178.65098 | 2003.0 |
| 277.002 | 2009.0 |
| 288.70485 | 2002.0 |
| 228.91057 | 2006.0 |
| 204.06812 | 0.0 |
| 212.40118 | 0.0 |
| 224.31302 | 2008.0 |
| 195.7873 | 1985.0 |
| 244.63628 | 2005.0 |
| 241.81506 | 0.0 |
| 224.10404 | 2001.0 |
| 132.75383 | 2008.0 |
| 113.3971 | 0.0 |
| 237.03465 | 0.0 |
| 162.58567 | 1987.0 |
| 247.24853 | 2008.0 |
| 285.30893 | 0.0 |
| 318.24934 | 0.0 |
| 375.53587 | 2007.0 |
| 188.78649 | 0.0 |
| 108.79955 | 0.0 |
| 270.91546 | 0.0 |
| 249.23383 | 0.0 |
| 192.80934 | 1984.0 |
| 295.20934 | 0.0 |
| 177.84118 | 2006.0 |
| 242.6771 | 0.0 |
| 245.28934 | 1999.0 |
| 105.61261 | 0.0 |
| 329.29914 | 0.0 |
| 207.46404 | 0.0 |
| 225.51465 | 2007.0 |
| 123.8722 | 0.0 |
| 270.10567 | 2008.0 |
| 174.86322 | 0.0 |
| 377.28608 | 0.0 |
| 220.18567 | 2005.0 |
| 1190.53016 | 0.0 |
| 1518.65424 | 0.0 |
| 438.64771 | 2008.0 |
| 344.842 | 2001.0 |
| 76.48608 | 1994.0 |
| 174.52363 | 2002.0 |
| 581.14567 | 0.0 |
| 177.68444 | 0.0 |
| 125.962 | 0.0 |
| 160.39138 | 2006.0 |
| 211.27791 | 0.0 |
| 182.88281 | 0.0 |
| 261.53751 | 2005.0 |
| 285.80526 | 0.0 |
| 263.44444 | 1991.0 |
| 133.32853 | 1998.0 |
| 313.99138 | 1990.0 |
| 199.18322 | 0.0 |
| 200.98567 | 0.0 |
| 170.84036 | 2009.0 |
| 194.48118 | 0.0 |
| 241.65832 | 0.0 |
| 245.15873 | 1970.0 |
| 262.66077 | 2002.0 |
| 307.46077 | 1999.0 |
| 295.20934 | 0.0 |
| 259.52608 | 0.0 |
| 347.19302 | 0.0 |
| 206.91546 | 0.0 |
| 399.51628 | 2008.0 |
| 271.25506 | 0.0 |
| 172.7473 | 1991.0 |
| 231.65342 | 1993.0 |
| 208.1171 | 1991.0 |
| 195.76118 | 1983.0 |
| 723.27791 | 1970.0 |
| 282.95791 | 0.0 |
| 153.12934 | 0.0 |
| 207.15057 | 0.0 |
| 174.41914 | 0.0 |
| 269.29587 | 2005.0 |
| 275.3824 | 0.0 |
| 149.41995 | 0.0 |
| 108.35546 | 1963.0 |
| 243.69587 | 0.0 |
| 308.27057 | 1996.0 |
| 204.90404 | 2007.0 |
| 311.24853 | 2001.0 |
| 164.77995 | 0.0 |
| 449.51465 | 0.0 |
| 140.93016 | 0.0 |
| 165.22404 | 2005.0 |
| 53.26322 | 2007.0 |
| 218.80118 | 2005.0 |
| 300.85179 | 1991.0 |
| 388.75383 | 2007.0 |
| 150.77832 | 1970.0 |
| 293.11955 | 0.0 |
| 177.71057 | 0.0 |
| 184.11057 | 2005.0 |
| 225.17506 | 2009.0 |
| 272.19546 | 2002.0 |
| 157.67465 | 0.0 |
| 204.61669 | 0.0 |
| 93.98812 | 0.0 |
| 204.45995 | 0.0 |
| 307.1473 | 0.0 |
| 347.0624 | 1970.0 |
| 184.73751 | 2005.0 |
| 146.65098 | 0.0 |
| 513.90649 | 2001.0 |
| 293.85098 | 1970.0 |
| 121.73016 | 2003.0 |
| 86.72608 | 0.0 |
| 171.25832 | 2007.0 |
| 264.95955 | 2007.0 |
| 411.68934 | 0.0 |
| 190.79791 | 1971.0 |
| 159.65995 | 0.0 |
| 162.89914 | 0.0 |
| 205.97506 | 2006.0 |
| 204.59057 | 0.0 |
| 117.02812 | 0.0 |
| 135.28771 | 0.0 |
| 163.65669 | 0.0 |
| 254.95465 | 0.0 |
| 178.31138 | 2001.0 |
| 150.77832 | 2001.0 |
| 410.53995 | 2001.0 |
| 222.30159 | 0.0 |
| 314.74893 | 0.0 |
| 233.11628 | 0.0 |
| 226.21995 | 0.0 |
| 441.67791 | 0.0 |
| 120.99873 | 2009.0 |
| 157.75302 | 0.0 |
| 203.65016 | 0.0 |
| 287.73832 | 0.0 |
| 226.7424 | 1997.0 |
| 69.56363 | 1993.0 |
| 174.52363 | 2007.0 |
| 363.67628 | 0.0 |
| 136.48934 | 0.0 |
| 390.60853 | 0.0 |
| 284.60363 | 0.0 |
| 291.81342 | 1990.0 |
| 502.7522 | 0.0 |
| 197.27628 | 0.0 |
| 329.53424 | 2009.0 |
| 340.1922 | 2003.0 |
| 170.94485 | 0.0 |
| 113.57995 | 0.0 |
| 205.24363 | 2009.0 |
| 169.22077 | 1994.0 |
| 285.70077 | 1980.0 |
| 221.23057 | 0.0 |
| 310.38649 | 0.0 |
| 353.48853 | 2008.0 |
| 415.92118 | 0.0 |
| 150.59546 | 0.0 |
| 236.90404 | 0.0 |
| 227.42159 | 1981.0 |
| 229.8771 | 1995.0 |
| 359.3922 | 0.0 |
| 403.17342 | 1998.0 |
| 296.59383 | 1997.0 |
| 117.65506 | 0.0 |
| 241.3971 | 0.0 |
| 34.92526 | 0.0 |
| 188.31628 | 0.0 |
| 409.02485 | 2002.0 |
| 335.5424 | 0.0 |
| 354.63791 | 0.0 |
| 213.31546 | 2007.0 |
| 238.62812 | 0.0 |
| 193.33179 | 1972.0 |
| 225.33179 | 0.0 |
| 166.84363 | 0.0 |
| 79.96036 | 1990.0 |
| 158.69342 | 2000.0 |
| 176.53506 | 0.0 |
| 347.61098 | 1999.0 |
| 106.39628 | 1994.0 |
| 147.93098 | 0.0 |
| 446.92853 | 0.0 |
| 360.22812 | 0.0 |
| 214.56934 | 0.0 |
| 325.35465 | 0.0 |
| 413.23057 | 0.0 |
| 218.04363 | 2001.0 |
| 215.30077 | 2002.0 |
| 57.44281 | 0.0 |
| 247.48363 | 2006.0 |
| 793.25995 | 0.0 |
| 467.3824 | 0.0 |
| 327.00036 | 1984.0 |
| 232.72444 | 2006.0 |
| 251.68934 | 0.0 |
| 197.3024 | 0.0 |
| 193.88036 | 0.0 |
| 383.32036 | 2004.0 |
| 269.71383 | 0.0 |
| 255.05914 | 0.0 |
| 337.18812 | 2009.0 |
| 240.92689 | 0.0 |
| 206.18404 | 2002.0 |
| 143.22893 | 0.0 |
| 244.27057 | 1980.0 |
| 83.56526 | 0.0 |
| 428.40771 | 0.0 |
| 261.11955 | 2007.0 |
| 208.37832 | 2008.0 |
| 369.78893 | 0.0 |
| 47.17669 | 2006.0 |
| 239.3073 | 0.0 |
| 17.37098 | 1993.0 |
| 257.04444 | 0.0 |
| 198.63465 | 0.0 |
| 208.40444 | 2002.0 |
| 338.28526 | 2005.0 |
| 175.15057 | 0.0 |
| 234.97098 | 0.0 |
| 275.06893 | 0.0 |
| 186.46159 | 0.0 |
| 201.74322 | 0.0 |
| 237.58322 | 0.0 |
| 219.402 | 0.0 |
| 461.29587 | 0.0 |
| 196.67546 | 0.0 |
| 290.63791 | 0.0 |
| 328.22812 | 0.0 |
| 260.64934 | 1981.0 |
| 245.83791 | 0.0 |
| 97.54077 | 0.0 |
| 248.0322 | 0.0 |
| 175.33342 | 1998.0 |
| 199.57506 | 0.0 |
| 229.45914 | 2005.0 |
| 902.26893 | 2000.0 |
| 271.12444 | 1991.0 |
| 211.17342 | 0.0 |
| 179.3824 | 1967.0 |
| 156.96934 | 0.0 |
| 281.0771 | 1998.0 |
| 291.97016 | 0.0 |
| 392.85506 | 0.0 |
| 223.00689 | 0.0 |
| 269.94893 | 1987.0 |
| 36.64934 | 1996.0 |
| 309.26322 | 0.0 |
| 178.41587 | 0.0 |
| 206.75873 | 2006.0 |
| 155.68934 | 1971.0 |
| 254.71955 | 1993.0 |
| 133.11955 | 0.0 |
| 260.362 | 0.0 |
| 135.28771 | 1984.0 |
| 158.27546 | 2005.0 |
| 154.93179 | 0.0 |
| 205.84444 | 2005.0 |
| 276.21832 | 0.0 |
| 193.61914 | 0.0 |
| 153.73016 | 1997.0 |
| 389.11955 | 0.0 |
| 195.23873 | 2007.0 |
| 210.72934 | 1995.0 |
| 336.06485 | 0.0 |
| 263.02649 | 0.0 |
| 230.26893 | 2001.0 |
| 40.6722 | 2007.0 |
| 255.92118 | 2009.0 |
| 305.60608 | 1995.0 |
| 177.8673 | 0.0 |
| 361.11628 | 0.0 |
| 357.66812 | 0.0 |
| 196.49261 | 2004.0 |
| 218.40934 | 1992.0 |
| 91.58485 | 0.0 |
| 185.25995 | 0.0 |
| 282.80118 | 0.0 |
| 244.68853 | 0.0 |
| 215.40526 | 1993.0 |
| 211.19955 | 1978.0 |
| 327.75791 | 0.0 |
| 510.40608 | 2005.0 |
| 212.74077 | 2009.0 |
| 120.86812 | 2008.0 |
| 507.08853 | 2001.0 |
| 265.11628 | 0.0 |
| 183.06567 | 0.0 |
| 199.54893 | 1982.0 |
| 41.92608 | 0.0 |
| 164.75383 | 0.0 |
| 267.33669 | 0.0 |
| 208.74404 | 1984.0 |
| 253.09995 | 2007.0 |
| 244.50567 | 0.0 |
| 195.73506 | 2007.0 |
| 160.07791 | 2007.0 |
| 327.70567 | 0.0 |
| 174.86322 | 0.0 |
| 272.92689 | 2005.0 |
| 251.53261 | 0.0 |
| 216.99873 | 0.0 |
| 195.3171 | 0.0 |
| 247.11791 | 0.0 |
| 101.3024 | 0.0 |
| 315.97669 | 2003.0 |
| 449.67138 | 0.0 |
| 173.16526 | 1998.0 |
| 394.44853 | 0.0 |
| 226.69016 | 2007.0 |
| 219.11465 | 0.0 |
| 240.92689 | 1997.0 |
| 227.91791 | 1999.0 |
| 119.84934 | 0.0 |
| 109.92281 | 1997.0 |
| 116.08771 | 0.0 |
| 187.71546 | 1975.0 |
| 191.65995 | 0.0 |
| 116.32281 | 1979.0 |
| 482.45506 | 0.0 |
| 262.71302 | 2003.0 |
| 208.97914 | 2005.0 |
| 209.81506 | 1975.0 |
| 129.85424 | 2002.0 |
| 219.0624 | 0.0 |
| 500.4273 | 2010.0 |
| 224.7571 | 0.0 |
| 274.85995 | 2003.0 |
| 145.162 | 1993.0 |
| 211.27791 | 0.0 |
| 167.49669 | 1981.0 |
| 415.7122 | 0.0 |
| 346.33098 | 0.0 |
| 399.69914 | 1999.0 |
| 136.56771 | 0.0 |
Exercises
- Why do you think average song durations increase dramatically in 70's?
- Add error bars with standard deviation around each average point in the plot.
- How did average loudness change over time?
- How did tempo change over time?
- What other aspects of songs can you explore with this technique?
Sampling and visualizing
You can dive deep into Scala visualisations here: - https://docs.databricks.com/notebooks/visualizations/charts-and-graphs-scala.html
You can also use R and Python for visualisations in the same notebook: - https://docs.databricks.com/notebooks/visualizations/index.html
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
Stage 3: Modeling Songs via k-means

This is the third step into our project. In the first step we parsed raw text files and created a table. Then we explored different aspects of data and learned that things have been changing over time. In this step we attempt to gain deeper understanding of our data by categorizing (a.k.a. clustering) our data. For the sake of training we pick a fairly simple model based on only three parameters. We leave more sophisticated modeling as exercies to the reader
We pick the most commonly used and simplest clustering algorithm (KMeans) for our job. The SparkML KMeans implementation expects input in a vector column. Fortunately, there are already utilities in SparkML that can help us convert existing columns in our table to a vector field. It is called VectorAssembler. Here we import that functionality and use it to create a new DataFrame
// Let's quickly do everything to register the tempView of the table here
// this is a case class for our row objects
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
def parseLine(line: String): Song = {
// this is robust parsing by try-catching type exceptions
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
// splitting the sting of each line by the delimiter TAB character '\t'
val tokens = line.split("\t")
// making song objects
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/datasets/sds/songs/data-001/part-*")
// .. fill in comment
val df = dataRDD.map(parseLine).toDF
// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[106] at textFile at command-2971213210277067:32
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
import org.apache.spark.ml.feature.VectorAssembler
val trainingData = new VectorAssembler()
.setInputCols(Array("duration", "tempo", "loudness"))
.setOutputCol("features")
.transform(table("songsTable"))
import org.apache.spark.ml.feature.VectorAssembler
trainingData: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 19 more fields]
All we have done above with the VectorAssembler method is:
- created a DataFrame called
trainingData - that
transformed ourtablecalledsongsTable - by adding an output column named
featuresusingsetOutputCol("features") - that was obtained from an
Arrayof thesongsTable's columns namedduration,tempoandloudnessusingsetInputCols(Array("duration", "tempo", "loudness")).
trainingData.take(3) // see first 3 rows of trainingData DataFrame, notice the vectors in the last column
res3: Array[org.apache.spark.sql.Row] = Array([AR81V6H1187FB48872,0.0,0.0,,Earl Sixteen,213.7073,0.0,11,0.419,-12.106,Soldier of Jah Army,0.0,SOVNZSZ12AB018A9B8,208.289,125.882,1.0,0.0,Rastaman,2003.0,-1,[213.7073,125.882,-12.106]], [ARVVZQP11E2835DBCB,0.0,0.0,,Wavves,133.25016,0.0,0,0.282,0.596,Wavvves,0.471578247701,SOJTQHQ12A8C143C5F,128.116,89.519,1.0,0.0,I Want To See You (And Go To The Movies),2009.0,-1,[133.25016,89.519,0.596]], [ARFG9M11187FB3BBCB,0.0,0.0,Nashua USA,C-Side,247.32689,0.0,9,0.612,-4.896,Santa Festival Compilation 2008 vol.1,0.0,SOAJSQL12AB0180501,242.196,171.278,5.0,1.0,Loose on the Dancefloor,0.0,225261,[247.32689,171.278,-4.896]])
Transformers
A Transformer is an abstraction that includes feature transformers and learned models. Technically, a Transformer implements a method transform(), which converts one DataFrame into another, generally by appending one or more columns. For example:
- A feature transformer might take a DataFrame, read a column (e.g., text), map it into a new column (e.g., feature vectors), and output a new DataFrame with the mapped column appended.
- A learning model might take a DataFrame, read the column containing feature vectors, predict the label for each feature vector, and output a new DataFrame with predicted labels appended as a column.
Estimators
An Estimator abstracts the concept of a learning algorithm or any algorithm that fits or trains on data.
Technically, an Estimator implements a method fit(), which accepts a DataFrame and produces a Model, which is a Transformer.
For example, a learning algorithm such as LogisticRegression is an Estimator, and calling fit() trains a LogisticRegressionModel, which is a Model and hence a Transformer.
display(trainingData.select("duration", "tempo", "loudness", "features").limit(5)) // see features in more detail
Demonstration of the standard algorithm
(1) (2)
(3)
(4)
- k initial "means" (in this case k=3) are randomly generated within the data domain (shown in color).
- k clusters are created by associating every observation with the nearest mean. The partitions here represent the Voronoi diagram generated by the means.
- The centroid of each of the k clusters becomes the new mean.
- Steps 2 and 3 are repeated until local convergence has been reached.
The "assignment" step 2 is also referred to as expectation step, the "update step" 3 as maximization step, making this algorithm a variant of the generalized expectation-maximization algorithm.
Caveats: As k-means is a heuristic algorithm, there is no guarantee that it will converge to the global optimum, and the result may depend on the initial clusters. As the algorithm is usually very fast, it is common to run it multiple times with different starting conditions. However, in the worst case, k-means can be very slow to converge. For more details see https://en.wikipedia.org/wiki/K-means_clustering that is also embedded in-place below.
CAUTION!
Iris flower data set, clustered using
- k-means (left) and
- true species in the data set (right).
k-means clustering result for the Iris flower data set and actual species visualized using ELKI. Cluster means are marked using larger, semi-transparent symbols.
Note that k-means is non-determinicstic, so results vary. Cluster means are visualized using larger, semi-transparent markers. The visualization was generated using ELKI.
With some cautionary tales we go ahead with applying k-means to our dataset next.
We can now pass this new DataFrame to the KMeans model and ask it to categorize different rows in our data to two different classes (setK(2)). We place the model in a immutable value named model.
Note: This command performs multiple spark jobs (one job per iteration in the KMeans algorithm). You will see the progress bar starting over and over again.
import org.apache.spark.ml.clustering.KMeans
val model = new KMeans().setK(2).fit(trainingData) // 37 seconds in 5 workers cluster
import org.apache.spark.ml.clustering.KMeans
model: org.apache.spark.ml.clustering.KMeansModel = KMeansModel: uid=kmeans_a1617b1902bd, k=2, distanceMeasure=euclidean, numFeatures=3
//model. // uncomment and place cursor next to . and hit Tab to see all methods on model
model.clusterCenters // get cluster centres
res5: Array[org.apache.spark.ml.linalg.Vector] = Array([208.72069181761955,124.38376303683947,-9.986137113920133], [441.1413218945455,123.00973381818183,-10.560375818181816])
val modelTransformed = model.transform(trainingData) // to get predictions as last column
modelTransformed: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 20 more fields]
Remember that ML Pipelines works with DataFrames. So, our trainingData and modelTransformed are both DataFrames
trainingData.printSchema
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
|-- features: vector (nullable = true)
modelTransformed.printSchema
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
|-- features: vector (nullable = true)
|-- prediction: integer (nullable = false)
- The column
featuresthat we specified as output column to ourVectorAssemblercontains the features - The new column
predictionin modelTransformed contains the predicted output
val transformed = modelTransformed.select("duration", "tempo", "loudness", "prediction")
transformed: org.apache.spark.sql.DataFrame = [duration: double, tempo: double ... 2 more fields]
To comfortably visualize the data we produce a random sample. Remember the display() function? We can use it to produce a nicely rendered table of transformed DataFrame.
// just sampling the fraction 0.005 of all the rows at random,
// 'false' argument to sample is for sampling without replacement
display(transformed.sample(false, fraction = 0.005, 12988934L))
| duration | tempo | loudness | prediction |
|---|---|---|---|
| 265.97832 | 129.987 | -5.065 | 0.0 |
| 306.36363 | 127.529 | -8.273 | 0.0 |
| 195.73506 | 115.191 | -6.327 | 0.0 |
| 257.82812 | 92.892 | -13.621 | 0.0 |
| 177.55383 | 149.853 | -10.969 | 0.0 |
| 224.9922 | 97.98 | -5.921 | 0.0 |
| 528.09098 | 160.022 | -5.956 | 1.0 |
| 213.68118 | 100.219 | -4.528 | 0.0 |
| 35.52608 | 121.349 | -18.721 | 0.0 |
| 213.68118 | 120.581 | -10.633 | 0.0 |
| 300.53832 | 139.017 | -13.319 | 0.0 |
| 138.81424 | 160.044 | -11.138 | 0.0 |
| 413.962 | 89.588 | -13.143 | 1.0 |
| 292.17914 | 188.543 | -5.389 | 0.0 |
| 419.34322 | 140.008 | -5.389 | 1.0 |
| 261.61587 | 152.951 | -12.447 | 0.0 |
| 168.64608 | 99.617 | -8.164 | 0.0 |
| 446.27546 | 196.005 | -6.621 | 1.0 |
| 343.92771 | 122.877 | -10.264 | 1.0 |
| 261.69424 | 145.129 | -4.733 | 0.0 |
| 408.47628 | 61.259 | -11.244 | 1.0 |
| 243.3824 | 161.778 | -6.88 | 0.0 |
| 184.00608 | 111.461 | -9.788 | 0.0 |
| 67.39546 | 188.723 | -3.239 | 0.0 |
| 165.51138 | 81.125 | -18.692 | 0.0 |
| 214.85669 | 144.174 | -17.989 | 0.0 |
| 185.96526 | 186.897 | -6.041 | 0.0 |
| 170.05669 | 75.356 | -16.486 | 0.0 |
| 193.72363 | 117.131 | -9.822 | 0.0 |
| 227.23873 | 91.953 | -11.221 | 0.0 |
| 177.6322 | 150.962 | -7.288 | 0.0 |
| 117.21098 | 100.021 | -10.782 | 0.0 |
| 422.42567 | 91.214 | -8.872 | 1.0 |
| 145.162 | 95.388 | -10.166 | 0.0 |
| 242.93832 | 112.015 | -7.135 | 0.0 |
| 366.00118 | 65.05 | -13.768 | 1.0 |
| 601.15546 | 70.623 | -11.179 | 1.0 |
| 196.17914 | 121.563 | -5.434 | 0.0 |
| 399.35955 | 124.963 | -3.415 | 1.0 |
| 287.73832 | 161.648 | -14.721 | 0.0 |
| 285.09995 | 132.86 | -10.23 | 0.0 |
| 358.32118 | 132.874 | -6.071 | 1.0 |
| 407.71873 | 85.996 | -10.103 | 1.0 |
| 463.25506 | 95.083 | -6.4 | 1.0 |
| 295.91465 | 93.515 | -8.987 | 0.0 |
| 273.8673 | 190.044 | -10.556 | 0.0 |
| 280.73751 | 113.363 | -6.893 | 0.0 |
| 142.65424 | 206.067 | -7.996 | 0.0 |
| 197.38077 | 84.023 | -10.964 | 0.0 |
| 226.24608 | 166.768 | -5.941 | 0.0 |
| 152.47628 | 153.528 | -7.393 | 0.0 |
| 315.32363 | 139.919 | -7.438 | 0.0 |
| 329.03791 | 180.059 | -6.473 | 1.0 |
| 296.88118 | 137.976 | -5.49 | 0.0 |
| 250.77506 | 84.543 | -18.058 | 0.0 |
| 48.14322 | 52.814 | -11.617 | 0.0 |
| 290.16771 | 141.24 | -14.009 | 0.0 |
| 406.04689 | 133.997 | -9.685 | 1.0 |
| 124.57751 | 161.237 | -12.314 | 0.0 |
| 280.45016 | 154.003 | -8.094 | 0.0 |
| 472.31955 | 219.371 | -6.08 | 1.0 |
| 267.4673 | 152.123 | -18.457 | 0.0 |
| 216.21506 | 88.022 | -7.096 | 0.0 |
| 31.00689 | 237.737 | -11.538 | 0.0 |
| 283.21914 | 132.934 | -11.153 | 0.0 |
| 284.21179 | 110.585 | -15.384 | 0.0 |
| 154.87955 | 157.881 | -4.377 | 0.0 |
| 431.46404 | 113.905 | -8.651 | 1.0 |
| 215.87546 | 124.572 | -7.67 | 0.0 |
| 161.43628 | 43.884 | -14.936 | 0.0 |
| 29.09995 | 135.146 | -1.837 | 0.0 |
| 301.68771 | 123.672 | -9.293 | 0.0 |
| 170.10893 | 124.022 | -9.589 | 0.0 |
| 281.96526 | 63.544 | -8.25 | 0.0 |
| 484.54485 | 149.565 | -5.501 | 1.0 |
| 135.1571 | 92.749 | -7.881 | 0.0 |
| 180.45342 | 137.899 | -5.246 | 0.0 |
| 142.44526 | 85.406 | -11.305 | 0.0 |
| 209.3971 | 93.233 | -13.079 | 0.0 |
| 319.9473 | 122.936 | -5.98 | 0.0 |
| 55.82322 | 249.325 | -8.656 | 0.0 |
| 192.88771 | 124.006 | -5.745 | 0.0 |
| 624.14322 | 128.988 | -7.872 | 1.0 |
| 188.49914 | 107.264 | -13.24 | 0.0 |
| 148.32281 | 150.075 | -5.793 | 0.0 |
| 195.00363 | 49.31 | -14.54 | 0.0 |
| 130.61179 | 100.208 | -10.487 | 0.0 |
| 212.11383 | 111.048 | -4.749 | 0.0 |
| 204.56444 | 92.076 | -7.906 | 0.0 |
| 292.04853 | 135.932 | -9.004 | 0.0 |
| 377.83465 | 73.336 | -13.457 | 1.0 |
| 246.96118 | 116.761 | -8.117 | 0.0 |
| 90.30485 | 39.3 | -15.444 | 0.0 |
| 201.92608 | 123.558 | -9.857 | 0.0 |
| 282.06975 | 136.09 | -6.202 | 0.0 |
| 194.55955 | 119.488 | -16.454 | 0.0 |
| 318.4322 | 140.467 | -6.375 | 0.0 |
| 55.45751 | 121.476 | -7.891 | 0.0 |
| 275.30404 | 90.024 | -6.191 | 0.0 |
| 422.24281 | 131.994 | -8.786 | 1.0 |
| 269.47873 | 88.921 | -4.419 | 0.0 |
| 186.01751 | 55.271 | -16.153 | 0.0 |
| 221.17832 | 94.967 | -6.591 | 0.0 |
| 275.66975 | 94.999 | -16.843 | 0.0 |
| 335.28118 | 90.129 | -13.33 | 1.0 |
| 288.88771 | 102.96 | -10.967 | 0.0 |
| 190.71955 | 88.269 | -13.167 | 0.0 |
| 211.17342 | 126.741 | -10.398 | 0.0 |
| 116.92363 | 140.09 | -7.578 | 0.0 |
| 310.282 | 181.753 | -17.61 | 0.0 |
| 184.21506 | 112.336 | -8.764 | 0.0 |
| 248.21506 | 101.988 | -6.487 | 0.0 |
| 116.00934 | 84.793 | -9.057 | 0.0 |
| 299.88526 | 143.66 | -7.279 | 0.0 |
| 1376.78322 | 107.066 | -7.893 | 1.0 |
| 182.85669 | 191.559 | -10.367 | 0.0 |
| 238.2624 | 48.592 | -9.687 | 0.0 |
| 239.5424 | 105.47 | -9.258 | 0.0 |
| 195.16036 | 127.911 | -7.054 | 0.0 |
| 149.73342 | 118.643 | -10.526 | 0.0 |
| 424.30649 | 62.8 | -22.35 | 1.0 |
| 221.77914 | 104.983 | -7.908 | 0.0 |
| 407.7971 | 126.983 | -11.675 | 1.0 |
| 175.04608 | 120.542 | -17.484 | 0.0 |
| 306.65098 | 120.0 | -18.973 | 0.0 |
| 315.0624 | 85.82 | -11.858 | 0.0 |
| 88.5024 | 211.429 | -11.543 | 0.0 |
| 101.61587 | 19.215 | -26.402 | 0.0 |
| 132.04853 | 186.123 | -7.21 | 0.0 |
| 146.25914 | 200.16 | -15.88 | 0.0 |
| 147.3824 | 104.658 | -8.836 | 0.0 |
| 156.26404 | 135.353 | -8.245 | 0.0 |
| 66.2722 | 115.129 | -16.416 | 0.0 |
| 247.43138 | 124.991 | -11.79 | 0.0 |
| 282.90567 | 115.472 | -12.448 | 0.0 |
| 401.05751 | 125.015 | -9.465 | 1.0 |
| 156.08118 | 88.128 | -16.916 | 0.0 |
| 198.08608 | 133.961 | -8.055 | 0.0 |
| 214.09914 | 84.353 | -12.991 | 0.0 |
| 269.73995 | 161.953 | -3.091 | 0.0 |
| 199.44444 | 169.922 | -6.241 | 0.0 |
| 149.65506 | 190.802 | -30.719 | 0.0 |
| 227.23873 | 120.14 | -12.39 | 0.0 |
To generate a scatter plot matrix, click on the plot button bellow the table and select scatter. That will transform your table to a scatter plot matrix. It automatically picks all numeric columns as values. To include predicted clusters, click on Plot Options and drag prediction to the list of Keys. You will get the following plot. On the diagonal panels you see the PDF of marginal distribution of each variable. Non-diagonal panels show a scatter plot between variables of the two variables of the row and column. For example the top right panel shows the scatter plot between duration and loudness. Each point is colored according to the cluster it is assigned to.
display(transformed.sample(false, fraction = 0.01)) // try fraction=1.0 as this dataset is small
| duration | tempo | loudness | prediction |
|---|---|---|---|
| 189.93587 | 134.957 | -12.934 | 0.0 |
| 206.44526 | 135.26 | -11.146 | 0.0 |
| 295.65342 | 137.904 | -7.308 | 0.0 |
| 158.1971 | 174.831 | -12.544 | 0.0 |
| 259.99628 | 171.094 | -12.057 | 0.0 |
| 277.002 | 88.145 | -6.039 | 0.0 |
| 1518.65424 | 65.003 | -19.182 | 1.0 |
| 121.73016 | 85.041 | -8.193 | 0.0 |
| 205.97506 | 106.952 | -6.572 | 0.0 |
| 206.18404 | 89.447 | -9.535 | 0.0 |
| 202.52689 | 134.13 | -6.535 | 0.0 |
| 259.02975 | 97.948 | -11.276 | 0.0 |
| 308.53179 | 80.017 | -12.277 | 0.0 |
| 151.71873 | 117.179 | -12.394 | 0.0 |
| 165.56363 | 99.187 | -11.414 | 0.0 |
| 370.83383 | 67.495 | -20.449 | 1.0 |
| 238.65424 | 100.071 | -9.276 | 0.0 |
| 163.7873 | 119.924 | -5.941 | 0.0 |
| 279.32689 | 67.371 | -26.67 | 0.0 |
| 353.85424 | 68.405 | -8.105 | 1.0 |
| 227.23873 | 117.62 | -7.874 | 0.0 |
| 195.49995 | 85.916 | -8.828 | 0.0 |
| 322.45506 | 135.826 | -5.034 | 0.0 |
| 235.88526 | 109.773 | -10.919 | 0.0 |
| 235.15383 | 163.924 | -9.666 | 0.0 |
| 253.67465 | 125.014 | -7.171 | 0.0 |
| 415.03302 | 71.991 | -15.224 | 1.0 |
| 190.85016 | 152.805 | -2.871 | 0.0 |
| 290.76853 | 84.841 | -6.161 | 0.0 |
| 346.8273 | 129.981 | -10.377 | 1.0 |
| 174.70649 | 143.446 | -11.994 | 0.0 |
| 170.84036 | 70.033 | -7.782 | 0.0 |
| 517.66812 | 151.948 | -12.363 | 1.0 |
| 62.06649 | 154.406 | -9.387 | 0.0 |
| 505.96526 | 90.59 | -19.848 | 1.0 |
| 480.86159 | 0.0 | -11.707 | 1.0 |
| 330.13506 | 204.534 | -9.85 | 1.0 |
| 223.99955 | 110.3 | -12.339 | 0.0 |
| 200.98567 | 89.215 | -3.858 | 0.0 |
| 158.09261 | 178.826 | -13.681 | 0.0 |
| 186.46159 | 110.102 | -16.477 | 0.0 |
| 399.46404 | 125.03 | -10.786 | 1.0 |
| 118.64771 | 85.747 | -34.461 | 0.0 |
| 352.49587 | 91.139 | -9.13 | 1.0 |
| 150.15138 | 106.364 | -7.337 | 0.0 |
| 158.98077 | 124.54 | -17.405 | 0.0 |
| 122.17424 | 170.018 | -9.047 | 0.0 |
| 299.38893 | 91.976 | -6.266 | 0.0 |
| 197.95546 | 142.744 | -8.189 | 0.0 |
| 332.38159 | 89.071 | -5.08 | 1.0 |
| 309.78567 | 136.05 | -15.105 | 0.0 |
| 198.47791 | 82.546 | -12.594 | 0.0 |
| 254.17098 | 144.01 | -9.0 | 0.0 |
| 155.81995 | 233.022 | -8.978 | 0.0 |
| 176.53506 | 136.255 | -9.675 | 0.0 |
| 331.54567 | 221.511 | -13.778 | 1.0 |
| 140.72118 | 162.44 | -10.654 | 0.0 |
| 210.9122 | 120.039 | -5.391 | 0.0 |
| 197.27628 | 119.396 | -22.599 | 0.0 |
| 339.25179 | 96.842 | -16.207 | 1.0 |
| 355.97016 | 142.777 | -5.118 | 1.0 |
| 170.23955 | 55.107 | -17.654 | 0.0 |
| 360.01914 | 119.997 | -7.53 | 1.0 |
| 241.76281 | 101.938 | -3.765 | 0.0 |
| 84.92363 | 236.863 | -15.846 | 0.0 |
| 361.09016 | 120.121 | -5.861 | 1.0 |
| 262.37342 | 89.924 | -4.819 | 0.0 |
| 225.74975 | 139.994 | -11.505 | 0.0 |
| 246.9873 | 172.147 | -16.273 | 0.0 |
| 159.242 | 166.855 | -4.771 | 0.0 |
| 192.86159 | 148.297 | -13.423 | 0.0 |
| 140.90404 | 92.602 | -12.604 | 0.0 |
| 224.20853 | 71.879 | -16.615 | 0.0 |
| 235.78077 | 180.366 | -3.918 | 0.0 |
| 244.45342 | 105.077 | -5.004 | 0.0 |
| 210.57261 | 200.044 | -11.381 | 0.0 |
| 327.3922 | 112.023 | -11.341 | 1.0 |
| 168.38485 | 99.991 | -4.1 | 0.0 |
| 287.13751 | 137.154 | -12.948 | 0.0 |
| 148.63628 | 121.773 | -11.432 | 0.0 |
| 214.7522 | 124.437 | -5.834 | 0.0 |
| 197.43302 | 92.959 | -4.948 | 0.0 |
| 150.17751 | 112.0 | -5.968 | 0.0 |
| 152.86812 | 120.884 | -15.693 | 0.0 |
| 173.322 | 120.827 | -7.96 | 0.0 |
| 235.15383 | 85.519 | -8.172 | 0.0 |
| 299.41506 | 94.68 | -15.486 | 0.0 |
| 222.35383 | 156.046 | -8.885 | 0.0 |
| 235.85914 | 130.055 | -5.341 | 0.0 |
| 276.71465 | 170.059 | -2.926 | 0.0 |
| 136.07138 | 168.895 | -6.971 | 0.0 |
| 205.7922 | 129.57 | -6.113 | 0.0 |
| 268.72118 | 126.894 | -7.142 | 0.0 |
| 390.63465 | 232.08 | -5.493 | 1.0 |
| 242.59873 | 124.034 | -11.24 | 0.0 |
| 274.80771 | 108.178 | -21.456 | 0.0 |
| 157.25669 | 151.108 | -11.678 | 0.0 |
| 203.67628 | 140.02 | -5.939 | 0.0 |
| 302.47138 | 148.2 | -10.428 | 0.0 |
| 292.64934 | 106.006 | -3.538 | 0.0 |
| 245.002 | 135.997 | -10.6 | 0.0 |
| 181.57669 | 132.89 | -5.429 | 0.0 |
| 36.38812 | 65.132 | -12.621 | 0.0 |
| 656.14322 | 95.469 | -8.588 | 1.0 |
| 262.50404 | 88.025 | -4.359 | 0.0 |
| 261.95546 | 117.93 | -5.5 | 0.0 |
| 337.57995 | 122.08 | -6.658 | 1.0 |
| 373.08036 | 126.846 | -10.115 | 1.0 |
| 220.36853 | 157.934 | -13.192 | 0.0 |
| 357.22404 | 199.889 | -9.832 | 1.0 |
| 663.61424 | 143.681 | -9.066 | 1.0 |
| 155.08853 | 201.152 | -7.43 | 0.0 |
| 264.56771 | 133.7 | -11.767 | 0.0 |
| 262.53016 | 105.041 | -3.933 | 0.0 |
| 386.61179 | 132.644 | -8.34 | 1.0 |
| 224.41751 | 93.333 | -12.001 | 0.0 |
| 264.25424 | 117.547 | -6.134 | 0.0 |
| 12.82567 | 99.229 | -29.527 | 0.0 |
| 470.25587 | 135.99 | -7.403 | 1.0 |
| 213.7073 | 92.584 | -8.398 | 0.0 |
| 50.59873 | 108.989 | -10.006 | 0.0 |
| 273.162 | 114.014 | -8.527 | 0.0 |
| 285.1522 | 101.877 | -9.361 | 0.0 |
| 267.88526 | 123.903 | -10.177 | 0.0 |
| 334.54975 | 138.386 | -7.389 | 1.0 |
| 174.0273 | 95.084 | -21.944 | 0.0 |
| 242.15465 | 87.196 | -9.749 | 0.0 |
| 188.96934 | 188.967 | -3.288 | 0.0 |
| 480.67873 | 127.989 | -7.243 | 1.0 |
| 182.54322 | 72.894 | -17.837 | 0.0 |
| 231.1571 | 126.959 | -5.632 | 0.0 |
| 105.37751 | 136.156 | -13.607 | 0.0 |
| 125.88363 | 93.022 | -13.496 | 0.0 |
| 216.99873 | 151.866 | -7.47 | 0.0 |
| 176.50893 | 142.601 | -3.881 | 0.0 |
| 245.57669 | 123.186 | -3.609 | 0.0 |
| 191.37261 | 145.008 | -14.3 | 0.0 |
| 62.11873 | 80.961 | -12.453 | 0.0 |
| 274.80771 | 83.505 | -10.032 | 0.0 |
| 148.29669 | 92.154 | -13.542 | 0.0 |
| 241.47546 | 115.886 | -11.948 | 0.0 |
| 250.40934 | 100.001 | -18.335 | 0.0 |
| 154.56608 | 96.464 | -12.133 | 0.0 |
| 485.14567 | 84.633 | -8.812 | 1.0 |
| 307.22567 | 63.498 | -9.047 | 0.0 |
| 167.78404 | 122.92 | -13.528 | 0.0 |
| 345.10322 | 130.003 | -7.858 | 1.0 |
| 178.9122 | 111.13 | -7.33 | 0.0 |
| 237.29587 | 111.244 | -15.209 | 0.0 |
| 230.16444 | 74.335 | -12.244 | 0.0 |
| 230.50404 | 98.933 | -12.616 | 0.0 |
| 195.16036 | 131.037 | -12.769 | 0.0 |
| 130.01098 | 99.176 | -11.082 | 0.0 |
| 150.02077 | 97.501 | -2.945 | 0.0 |
| 239.64689 | 89.99 | -9.224 | 0.0 |
| 91.6371 | 71.187 | -16.523 | 0.0 |
| 136.88118 | 121.19 | -12.181 | 0.0 |
| 265.29914 | 167.132 | -13.859 | 0.0 |
| 166.84363 | 76.298 | -7.692 | 0.0 |
| 34.79465 | 109.386 | -13.591 | 0.0 |
| 344.29342 | 138.826 | -12.746 | 1.0 |
| 294.71302 | 123.963 | -15.225 | 0.0 |
| 232.9073 | 100.264 | -7.171 | 0.0 |
| 501.68118 | 126.947 | -8.972 | 1.0 |
| 163.00363 | 154.296 | -19.387 | 0.0 |
| 186.51383 | 89.989 | -5.338 | 0.0 |
| 279.30077 | 130.522 | -11.813 | 0.0 |
| 202.78812 | 79.515 | -26.44 | 0.0 |
| 123.45424 | 129.987 | -14.751 | 0.0 |
| 237.08689 | 82.135 | -6.347 | 0.0 |
| 239.75138 | 169.111 | -7.915 | 0.0 |
| 105.87383 | 145.028 | -2.414 | 0.0 |
| 250.46159 | 123.306 | -5.113 | 0.0 |
| 227.68281 | 132.51 | -6.661 | 0.0 |
| 487.44444 | 100.065 | -16.668 | 1.0 |
| 247.17016 | 165.886 | -8.476 | 0.0 |
| 194.92526 | 99.414 | -7.516 | 0.0 |
| 174.602 | 194.079 | -4.611 | 0.0 |
| 199.96689 | 148.78 | -9.657 | 0.0 |
| 278.7522 | 185.291 | -10.58 | 0.0 |
| 147.82649 | 88.634 | -3.83 | 0.0 |
| 166.1122 | 106.748 | -10.875 | 0.0 |
| 150.67383 | 120.367 | -14.628 | 0.0 |
| 237.53098 | 127.24 | -27.291 | 0.0 |
| 174.10567 | 124.185 | -6.983 | 0.0 |
| 84.84526 | 98.321 | -4.167 | 0.0 |
| 287.50322 | 121.821 | -9.951 | 0.0 |
| 142.23628 | 184.994 | -16.834 | 0.0 |
| 85.91628 | 180.788 | -9.972 | 0.0 |
| 217.23383 | 108.892 | -7.211 | 0.0 |
| 233.63873 | 113.878 | -6.394 | 0.0 |
| 291.94404 | 91.976 | -6.998 | 0.0 |
| 502.5171 | 140.015 | -5.285 | 1.0 |
| 316.02893 | 97.508 | -10.52 | 0.0 |
| 408.58077 | 176.977 | -15.701 | 1.0 |
| 244.13995 | 156.187 | -5.985 | 0.0 |
| 146.46812 | 140.032 | -13.347 | 0.0 |
| 131.05587 | 114.198 | -23.084 | 0.0 |
| 175.80363 | 137.052 | -7.147 | 0.0 |
| 230.53016 | 115.496 | -3.953 | 0.0 |
| 247.92771 | 90.092 | -10.103 | 0.0 |
| 207.04608 | 132.94 | -7.325 | 0.0 |
| 163.70893 | 77.698 | -18.806 | 0.0 |
| 179.722 | 81.884 | -40.036 | 0.0 |
| 214.02077 | 87.003 | -4.724 | 0.0 |
| 379.68934 | 127.986 | -8.715 | 1.0 |
| 173.322 | 74.929 | -3.089 | 0.0 |
| 263.81016 | 131.945 | -6.911 | 0.0 |
| 244.21832 | 109.004 | -3.762 | 0.0 |
| 220.1073 | 99.948 | -14.54 | 0.0 |
| 298.37016 | 87.591 | -31.42 | 0.0 |
| 273.97179 | 169.372 | -10.764 | 0.0 |
| 229.74649 | 117.184 | -6.174 | 0.0 |
| 383.68608 | 127.261 | -3.975 | 1.0 |
| 278.02077 | 47.779 | -4.486 | 0.0 |
| 135.75791 | 164.977 | -5.974 | 0.0 |
| 373.75955 | 129.057 | -8.391 | 1.0 |
| 272.40444 | 162.882 | -14.916 | 0.0 |
| 164.93669 | 120.317 | -11.476 | 0.0 |
| 242.99057 | 103.45 | -16.493 | 0.0 |
| 248.24118 | 116.026 | -6.973 | 0.0 |
| 304.61342 | 212.051 | -17.131 | 0.0 |
| 228.91057 | 91.879 | -23.314 | 0.0 |
| 308.50567 | 118.188 | -7.159 | 0.0 |
| 419.83955 | 125.007 | -11.017 | 1.0 |
| 189.67465 | 193.129 | -8.674 | 0.0 |
| 242.28526 | 69.129 | -16.121 | 0.0 |
| 213.02812 | 126.919 | -8.439 | 0.0 |
| 290.16771 | 135.96 | -7.305 | 0.0 |
| 143.12444 | 100.706 | -12.15 | 0.0 |
| 278.22975 | 103.227 | -7.35 | 0.0 |
| 260.30975 | 190.015 | -5.148 | 0.0 |
| 112.53506 | 128.054 | -18.969 | 0.0 |
| 248.78975 | 102.113 | -7.149 | 0.0 |
| 178.33751 | 147.659 | -3.494 | 0.0 |
| 242.83383 | 72.278 | -11.75 | 0.0 |
| 226.97751 | 100.012 | -4.935 | 0.0 |
| 172.61669 | 102.747 | -9.067 | 0.0 |
| 188.96934 | 110.208 | -7.915 | 0.0 |
| 129.35791 | 183.983 | -7.223 | 0.0 |
| 169.45587 | 91.872 | -5.507 | 0.0 |
| 57.67791 | 104.328 | -9.213 | 0.0 |
| 294.47791 | 107.29 | -6.285 | 0.0 |
| 372.79302 | 124.018 | -8.312 | 1.0 |
| 226.82077 | 132.001 | -5.636 | 0.0 |
| 171.25832 | 111.129 | -10.311 | 0.0 |
| 130.61179 | 204.936 | -9.571 | 0.0 |
| 123.48036 | 171.624 | -7.069 | 0.0 |
| 230.86975 | 154.048 | -4.541 | 0.0 |
| 181.26322 | 120.07 | -3.623 | 0.0 |
| 102.79138 | 113.374 | -13.804 | 0.0 |
| 385.64526 | 116.331 | -6.748 | 1.0 |
| 193.69751 | 112.542 | -4.375 | 0.0 |
| 249.20771 | 146.99 | -7.306 | 0.0 |
| 172.64281 | 119.993 | -9.333 | 0.0 |
| 152.45016 | 95.981 | -12.144 | 0.0 |
| 142.75873 | 86.66 | -14.115 | 0.0 |
| 413.20444 | 123.564 | -10.417 | 1.0 |
| 208.87465 | 134.331 | -14.099 | 0.0 |
| 200.51546 | 165.825 | -12.445 | 0.0 |
| 337.6322 | 115.649 | -11.181 | 1.0 |
| 222.9024 | 75.527 | -15.198 | 0.0 |
| 221.77914 | 104.983 | -7.908 | 0.0 |
| 262.922 | 156.13 | -13.043 | 0.0 |
| 206.602 | 236.05 | -3.612 | 0.0 |
| 277.9424 | 147.967 | -8.768 | 0.0 |
| 139.4673 | 159.421 | -18.781 | 0.0 |
| 167.73179 | 161.62 | -11.271 | 0.0 |
| 205.71383 | 190.11 | -3.171 | 0.0 |
| 308.55791 | 92.008 | -11.944 | 0.0 |
| 184.89424 | 111.551 | -11.777 | 0.0 |
| 162.42893 | 83.324 | -15.155 | 0.0 |
| 150.04689 | 119.14 | -23.358 | 0.0 |
| 217.15546 | 128.463 | -3.67 | 0.0 |
| 156.49914 | 155.053 | -3.135 | 0.0 |
| 720.74404 | 113.527 | -11.96 | 1.0 |
| 429.84444 | 146.456 | -10.661 | 1.0 |
| 258.61179 | 92.829 | -8.928 | 0.0 |
| 210.31138 | 86.073 | -16.77 | 0.0 |
| 263.02649 | 98.531 | -27.825 | 0.0 |
| 167.49669 | 90.052 | -10.736 | 0.0 |
| 358.08608 | 63.867 | -12.322 | 1.0 |
| 326.19057 | 140.026 | -4.9 | 1.0 |
| 418.42893 | 85.351 | -7.587 | 1.0 |
| 192.13016 | 147.428 | -8.522 | 0.0 |
| 180.61016 | 89.582 | -12.25 | 0.0 |
| 194.21995 | 137.415 | -21.924 | 0.0 |
| 194.5073 | 169.757 | -10.502 | 0.0 |
| 260.64934 | 193.875 | -7.698 | 0.0 |
| 246.38649 | 142.081 | -7.517 | 0.0 |
| 237.40036 | 196.665 | -9.895 | 0.0 |
| 183.71873 | 124.706 | -28.112 | 0.0 |
| 125.64853 | 94.467 | -4.691 | 0.0 |
| 217.0771 | 98.966 | -4.5 | 0.0 |
| 297.22077 | 107.963 | -16.542 | 0.0 |
| 153.57342 | 93.931 | -15.579 | 0.0 |
| 258.35057 | 145.774 | -7.775 | 0.0 |
| 213.13261 | 86.633 | -7.306 | 0.0 |
| 215.66649 | 111.961 | -5.529 | 0.0 |
| 84.4273 | 74.848 | -22.231 | 0.0 |
| 215.87546 | 105.793 | -8.988 | 0.0 |
| 165.48526 | 127.001 | -13.785 | 0.0 |
| 399.33342 | 161.057 | -3.206 | 1.0 |
| 367.93424 | 119.991 | -6.123 | 1.0 |
| 313.44281 | 129.342 | -10.202 | 0.0 |
| 295.67955 | 114.641 | -9.913 | 0.0 |
| 196.5971 | 96.798 | -14.001 | 0.0 |
| 191.4771 | 100.248 | -5.114 | 0.0 |
| 313.44281 | 102.699 | -7.444 | 0.0 |
| 205.11302 | 93.238 | -6.034 | 0.0 |
| 81.42322 | 207.494 | -24.804 | 0.0 |
| 188.78649 | 194.439 | -9.626 | 0.0 |
| 308.24444 | 145.975 | -6.359 | 0.0 |
| 411.92444 | 237.466 | -9.633 | 1.0 |
| 256.86159 | 131.837 | -11.147 | 0.0 |
| 38.45179 | 73.214 | -14.797 | 0.0 |
| 264.77669 | 130.674 | -4.721 | 0.0 |
| 212.4273 | 116.029 | -2.106 | 0.0 |
displayHTML(frameIt("https://en.wikipedia.org/wiki/Euclidean_space",500)) // make sure you run the cell with first wikipedia article!
Do you see the problem in our clusters based on the plot?
As you can see there is very little "separation" (in the sense of being separable into two point clouds, that represent our two identifed clusters, such that they have minimal overlay of these two features, i.e. tempo and loudness. NOTE that this sense of "pairwise separation" is a 2D projection of all three features in 3D Euclidean Space, i.e. loudness, tempo and duration, that depends directly on their two-dimensional visually sense-making projection of perhaps two important song features, as depicted in the corresponding 2D-scatter-plot of tempo versus loudness within the 2D scatter plot matrix that is helping us to partially visualize in the 2D-plane all of the three features in the three dimensional real-valued feature space that was the input to our K-Means algorithm) between loudness, and tempo and generated clusters. To see that, focus on the panels in the first and second columns of the scatter plot matrix. For varying values of loudness and tempo prediction does not change. Instead, duration of a song alone predicts what cluster it belongs to. Why is that?
To see the reason, let's take a look at the marginal distribution of duration in the next cell.
To produce this plot, we have picked histogram from the plots menu and in Plot Options we chose prediction as key and duration as value. The histogram plot shows significant skew in duration. Basically there are a few very long songs. These data points have large leverage on the mean function (what KMeans uses for clustering).
display(transformed.sample(false, fraction = 1.0).select("duration", "prediction")) // plotting over all results
| duration | prediction |
|---|---|
| 213.7073 | 0.0 |
| 133.25016 | 0.0 |
| 247.32689 | 0.0 |
| 337.05751 | 1.0 |
| 430.23628 | 1.0 |
| 186.80118 | 0.0 |
| 361.89995 | 1.0 |
| 220.00281 | 0.0 |
| 156.86485 | 0.0 |
| 256.67873 | 0.0 |
| 204.64281 | 0.0 |
| 112.48281 | 0.0 |
| 170.39628 | 0.0 |
| 215.95383 | 0.0 |
| 303.62077 | 0.0 |
| 266.60526 | 0.0 |
| 326.19057 | 1.0 |
| 51.04281 | 0.0 |
| 129.4624 | 0.0 |
| 253.33506 | 0.0 |
| 237.76608 | 0.0 |
| 132.96281 | 0.0 |
| 399.35955 | 1.0 |
| 168.75057 | 0.0 |
| 396.042 | 1.0 |
| 192.10404 | 0.0 |
| 12.2771 | 0.0 |
| 367.56853 | 1.0 |
| 189.93587 | 0.0 |
| 233.50812 | 0.0 |
| 462.68036 | 1.0 |
| 202.60526 | 0.0 |
| 241.52771 | 0.0 |
| 275.64363 | 0.0 |
| 350.69342 | 1.0 |
| 166.55628 | 0.0 |
| 249.49506 | 0.0 |
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| 182.88281 | 0.0 |
| 261.53751 | 0.0 |
| 285.80526 | 0.0 |
| 263.44444 | 0.0 |
| 133.32853 | 0.0 |
| 313.99138 | 0.0 |
| 199.18322 | 0.0 |
| 200.98567 | 0.0 |
| 170.84036 | 0.0 |
| 194.48118 | 0.0 |
| 241.65832 | 0.0 |
| 245.15873 | 0.0 |
| 262.66077 | 0.0 |
| 307.46077 | 0.0 |
| 295.20934 | 0.0 |
| 259.52608 | 0.0 |
| 347.19302 | 1.0 |
| 206.91546 | 0.0 |
| 399.51628 | 1.0 |
| 271.25506 | 0.0 |
| 172.7473 | 0.0 |
| 231.65342 | 0.0 |
| 208.1171 | 0.0 |
| 195.76118 | 0.0 |
| 723.27791 | 1.0 |
| 282.95791 | 0.0 |
| 153.12934 | 0.0 |
| 207.15057 | 0.0 |
| 174.41914 | 0.0 |
| 269.29587 | 0.0 |
| 275.3824 | 0.0 |
| 149.41995 | 0.0 |
| 108.35546 | 0.0 |
| 243.69587 | 0.0 |
| 308.27057 | 0.0 |
| 204.90404 | 0.0 |
| 311.24853 | 0.0 |
| 164.77995 | 0.0 |
| 449.51465 | 1.0 |
| 140.93016 | 0.0 |
| 165.22404 | 0.0 |
| 53.26322 | 0.0 |
| 218.80118 | 0.0 |
| 300.85179 | 0.0 |
| 388.75383 | 1.0 |
| 150.77832 | 0.0 |
| 293.11955 | 0.0 |
| 177.71057 | 0.0 |
| 184.11057 | 0.0 |
| 225.17506 | 0.0 |
| 272.19546 | 0.0 |
| 157.67465 | 0.0 |
| 204.61669 | 0.0 |
| 93.98812 | 0.0 |
| 204.45995 | 0.0 |
| 307.1473 | 0.0 |
| 347.0624 | 1.0 |
| 184.73751 | 0.0 |
| 146.65098 | 0.0 |
| 513.90649 | 1.0 |
| 293.85098 | 0.0 |
| 121.73016 | 0.0 |
| 86.72608 | 0.0 |
| 171.25832 | 0.0 |
| 264.95955 | 0.0 |
| 411.68934 | 1.0 |
| 190.79791 | 0.0 |
| 159.65995 | 0.0 |
| 162.89914 | 0.0 |
| 205.97506 | 0.0 |
| 204.59057 | 0.0 |
| 117.02812 | 0.0 |
| 135.28771 | 0.0 |
| 163.65669 | 0.0 |
| 254.95465 | 0.0 |
| 178.31138 | 0.0 |
| 150.77832 | 0.0 |
| 410.53995 | 1.0 |
| 222.30159 | 0.0 |
| 314.74893 | 0.0 |
| 233.11628 | 0.0 |
| 226.21995 | 0.0 |
| 441.67791 | 1.0 |
| 120.99873 | 0.0 |
| 157.75302 | 0.0 |
| 203.65016 | 0.0 |
| 287.73832 | 0.0 |
| 226.7424 | 0.0 |
| 69.56363 | 0.0 |
| 174.52363 | 0.0 |
| 363.67628 | 1.0 |
| 136.48934 | 0.0 |
| 390.60853 | 1.0 |
| 284.60363 | 0.0 |
| 291.81342 | 0.0 |
| 502.7522 | 1.0 |
| 197.27628 | 0.0 |
| 329.53424 | 1.0 |
| 340.1922 | 1.0 |
| 170.94485 | 0.0 |
| 113.57995 | 0.0 |
| 205.24363 | 0.0 |
| 169.22077 | 0.0 |
| 285.70077 | 0.0 |
| 221.23057 | 0.0 |
| 310.38649 | 0.0 |
| 353.48853 | 1.0 |
| 415.92118 | 1.0 |
| 150.59546 | 0.0 |
| 236.90404 | 0.0 |
| 227.42159 | 0.0 |
| 229.8771 | 0.0 |
| 359.3922 | 1.0 |
| 403.17342 | 1.0 |
| 296.59383 | 0.0 |
| 117.65506 | 0.0 |
| 241.3971 | 0.0 |
| 34.92526 | 0.0 |
| 188.31628 | 0.0 |
| 409.02485 | 1.0 |
| 335.5424 | 1.0 |
| 354.63791 | 1.0 |
| 213.31546 | 0.0 |
| 238.62812 | 0.0 |
| 193.33179 | 0.0 |
| 225.33179 | 0.0 |
| 166.84363 | 0.0 |
| 79.96036 | 0.0 |
| 158.69342 | 0.0 |
| 176.53506 | 0.0 |
| 347.61098 | 1.0 |
| 106.39628 | 0.0 |
| 147.93098 | 0.0 |
| 446.92853 | 1.0 |
| 360.22812 | 1.0 |
| 214.56934 | 0.0 |
| 325.35465 | 1.0 |
| 413.23057 | 1.0 |
| 218.04363 | 0.0 |
| 215.30077 | 0.0 |
| 57.44281 | 0.0 |
| 247.48363 | 0.0 |
| 793.25995 | 1.0 |
| 467.3824 | 1.0 |
| 327.00036 | 1.0 |
| 232.72444 | 0.0 |
| 251.68934 | 0.0 |
| 197.3024 | 0.0 |
| 193.88036 | 0.0 |
| 383.32036 | 1.0 |
| 269.71383 | 0.0 |
| 255.05914 | 0.0 |
| 337.18812 | 1.0 |
| 240.92689 | 0.0 |
| 206.18404 | 0.0 |
| 143.22893 | 0.0 |
| 244.27057 | 0.0 |
| 83.56526 | 0.0 |
| 428.40771 | 1.0 |
| 261.11955 | 0.0 |
| 208.37832 | 0.0 |
| 369.78893 | 1.0 |
| 47.17669 | 0.0 |
| 239.3073 | 0.0 |
| 17.37098 | 0.0 |
| 257.04444 | 0.0 |
| 198.63465 | 0.0 |
| 208.40444 | 0.0 |
| 338.28526 | 1.0 |
| 175.15057 | 0.0 |
| 234.97098 | 0.0 |
| 275.06893 | 0.0 |
| 186.46159 | 0.0 |
| 201.74322 | 0.0 |
| 237.58322 | 0.0 |
| 219.402 | 0.0 |
| 461.29587 | 1.0 |
| 196.67546 | 0.0 |
| 290.63791 | 0.0 |
| 328.22812 | 1.0 |
| 260.64934 | 0.0 |
| 245.83791 | 0.0 |
| 97.54077 | 0.0 |
| 248.0322 | 0.0 |
| 175.33342 | 0.0 |
| 199.57506 | 0.0 |
| 229.45914 | 0.0 |
| 902.26893 | 1.0 |
| 271.12444 | 0.0 |
| 211.17342 | 0.0 |
| 179.3824 | 0.0 |
| 156.96934 | 0.0 |
| 281.0771 | 0.0 |
| 291.97016 | 0.0 |
| 392.85506 | 1.0 |
| 223.00689 | 0.0 |
| 269.94893 | 0.0 |
| 36.64934 | 0.0 |
| 309.26322 | 0.0 |
| 178.41587 | 0.0 |
| 206.75873 | 0.0 |
| 155.68934 | 0.0 |
| 254.71955 | 0.0 |
| 133.11955 | 0.0 |
| 260.362 | 0.0 |
| 135.28771 | 0.0 |
| 158.27546 | 0.0 |
| 154.93179 | 0.0 |
| 205.84444 | 0.0 |
| 276.21832 | 0.0 |
| 193.61914 | 0.0 |
| 153.73016 | 0.0 |
| 389.11955 | 1.0 |
| 195.23873 | 0.0 |
| 210.72934 | 0.0 |
| 336.06485 | 1.0 |
| 263.02649 | 0.0 |
| 230.26893 | 0.0 |
| 40.6722 | 0.0 |
| 255.92118 | 0.0 |
| 305.60608 | 0.0 |
| 177.8673 | 0.0 |
| 361.11628 | 1.0 |
| 357.66812 | 1.0 |
| 196.49261 | 0.0 |
| 218.40934 | 0.0 |
| 91.58485 | 0.0 |
| 185.25995 | 0.0 |
| 282.80118 | 0.0 |
| 244.68853 | 0.0 |
| 215.40526 | 0.0 |
| 211.19955 | 0.0 |
| 327.75791 | 1.0 |
| 510.40608 | 1.0 |
| 212.74077 | 0.0 |
| 120.86812 | 0.0 |
| 507.08853 | 1.0 |
| 265.11628 | 0.0 |
| 183.06567 | 0.0 |
| 199.54893 | 0.0 |
| 41.92608 | 0.0 |
| 164.75383 | 0.0 |
| 267.33669 | 0.0 |
| 208.74404 | 0.0 |
| 253.09995 | 0.0 |
| 244.50567 | 0.0 |
| 195.73506 | 0.0 |
| 160.07791 | 0.0 |
| 327.70567 | 1.0 |
| 174.86322 | 0.0 |
| 272.92689 | 0.0 |
| 251.53261 | 0.0 |
| 216.99873 | 0.0 |
| 195.3171 | 0.0 |
| 247.11791 | 0.0 |
| 101.3024 | 0.0 |
| 315.97669 | 0.0 |
| 449.67138 | 1.0 |
| 173.16526 | 0.0 |
| 394.44853 | 1.0 |
| 226.69016 | 0.0 |
| 219.11465 | 0.0 |
| 240.92689 | 0.0 |
| 227.91791 | 0.0 |
| 119.84934 | 0.0 |
| 109.92281 | 0.0 |
| 116.08771 | 0.0 |
| 187.71546 | 0.0 |
| 191.65995 | 0.0 |
| 116.32281 | 0.0 |
| 482.45506 | 1.0 |
| 262.71302 | 0.0 |
| 208.97914 | 0.0 |
| 209.81506 | 0.0 |
| 129.85424 | 0.0 |
| 219.0624 | 0.0 |
| 500.4273 | 1.0 |
| 224.7571 | 0.0 |
| 274.85995 | 0.0 |
| 145.162 | 0.0 |
| 211.27791 | 0.0 |
| 167.49669 | 0.0 |
| 415.7122 | 1.0 |
| 346.33098 | 1.0 |
| 399.69914 | 1.0 |
| 136.56771 | 0.0 |
There are known techniques for dealing with skewed features. A simple technique is applying a power transformation. We are going to use the simplest and most common power transformation: logarithm.
In following cell we repeat the clustering experiment with a transformed DataFrame that includes a new column called log_duration.
val df = table("songsTable").selectExpr("tempo", "loudness", "log(duration) as log_duration")
val trainingData2 = new VectorAssembler().
setInputCols(Array("log_duration", "tempo", "loudness")).
setOutputCol("features").
transform(df)
val model2 = new KMeans().setK(2).fit(trainingData2)
val transformed2 = model2.transform(trainingData2).select("log_duration", "tempo", "loudness", "prediction")
display(transformed2.sample(false, fraction = 0.1))
| log_duration | tempo | loudness | prediction |
|---|---|---|---|
| 6.064334546146247 | 121.998 | -6.846 | 1.0 |
| 5.258037100266539 | 123.666 | -12.546 | 1.0 |
| 5.701132566749472 | 110.544 | -8.154 | 1.0 |
| 5.916880511654107 | 83.838 | -6.773 | 1.0 |
| 6.087263380229098 | 108.011 | -7.793 | 1.0 |
| 5.2087027312927825 | 169.892 | -11.187 | 0.0 |
| 5.492162232587351 | 84.753 | -6.803 | 1.0 |
| 5.471616846527706 | 107.832 | -8.638 | 1.0 |
| 5.363261820908567 | 117.974 | -3.807 | 1.0 |
| 6.079997540851444 | 115.995 | -10.967 | 1.0 |
| 5.330035292514153 | 135.26 | -11.146 | 0.0 |
| 5.23255879478462 | 90.885 | -8.762 | 1.0 |
| 5.098409146993903 | 109.509 | -11.688 | 1.0 |
| 4.9194948222974855 | 166.431 | -8.966 | 0.0 |
| 5.255177375047388 | 121.426 | -9.392 | 1.0 |
| 5.003433624211064 | 65.99 | -18.15 | 1.0 |
| 5.147586739998935 | 127.897 | -11.361 | 1.0 |
| 5.4924849397457205 | 116.808 | -8.526 | 1.0 |
| 5.91603579541654 | 107.923 | -13.386 | 1.0 |
| 5.315376921332985 | 172.282 | -11.732 | 0.0 |
| 5.0481981594199565 | 111.826 | -27.174 | 1.0 |
| 5.879897616998539 | 80.011 | -20.412 | 1.0 |
| 5.403094457046498 | 115.084 | -8.535 | 1.0 |
| 5.034688938362198 | 124.039 | -8.9 | 1.0 |
| 4.943992088552383 | 180.034 | -8.643 | 0.0 |
| 5.1808909048116165 | 121.008 | -6.684 | 1.0 |
| 5.863407230069623 | 133.41 | -11.714 | 0.0 |
| 5.404974606095917 | 105.109 | -6.202 | 1.0 |
| 5.88426887636043 | 114.692 | -15.428 | 1.0 |
| 4.493015195631618 | 108.378 | -5.904 | 1.0 |
| 5.752687806796722 | 90.654 | -7.9 | 1.0 |
| 5.347719391111762 | 191.267 | -9.878 | 0.0 |
| 5.343109071331969 | 155.839 | -13.898 | 0.0 |
| 4.394191590641474 | 81.892 | -17.253 | 1.0 |
| 4.879205158949818 | 167.916 | -10.475 | 0.0 |
| 6.019697811017462 | 162.481 | -4.921 | 0.0 |
| 5.451201144193524 | 140.804 | -16.023 | 0.0 |
| 5.212267799598076 | 126.594 | -11.339 | 1.0 |
| 5.060202299303682 | 103.591 | -9.935 | 1.0 |
| 5.029575588906314 | 174.223 | -10.233 | 0.0 |
| 5.2982824659390175 | 43.756 | -16.789 | 1.0 |
| 5.3881634635863005 | 120.944 | -8.862 | 1.0 |
| 5.492592526832023 | 219.306 | -20.349 | 0.0 |
| 5.571559292617606 | 131.909 | -13.378 | 0.0 |
| 6.411462243749755 | 147.183 | -7.165 | 0.0 |
| 5.997550987096423 | 135.003 | -14.34 | 0.0 |
| 6.600945526605945 | 62.98 | -15.22 | 1.0 |
| 5.142410119860136 | 136.089 | -7.688 | 0.0 |
| 6.005052313868685 | 101.182 | -7.541 | 1.0 |
| 5.267510727581979 | 129.956 | -3.927 | 1.0 |
| 5.786199622652282 | 127.09 | -4.866 | 1.0 |
| 5.07370048040891 | 80.762 | -11.391 | 1.0 |
| 5.518391532113626 | 30.832 | -9.864 | 1.0 |
| 5.220913615430304 | 98.901 | -17.031 | 1.0 |
| 6.249435250930559 | 67.841 | -19.533 | 1.0 |
| 5.885358726001259 | 193.1 | -11.081 | 0.0 |
| 4.704283488381446 | 106.153 | -7.04 | 1.0 |
| 5.520380974933744 | 130.191 | -5.42 | 1.0 |
| 5.519229670480138 | 105.352 | -6.023 | 1.0 |
| 5.117526637439253 | 93.552 | -12.528 | 1.0 |
| 5.623553066221829 | 101.951 | -5.001 | 1.0 |
| 5.302843607646548 | 95.944 | -14.027 | 1.0 |
| 5.245310235960805 | 140.057 | -7.697 | 0.0 |
| 5.263191047910029 | 80.464 | -18.496 | 1.0 |
| 5.343982892210335 | 110.041 | -8.578 | 1.0 |
| 5.6999969519310385 | 86.894 | -10.151 | 1.0 |
| 5.2573569249444265 | 132.12 | -20.601 | 0.0 |
| 5.587726858651023 | 89.001 | -9.355 | 1.0 |
| 5.624024726377879 | 88.145 | -6.039 | 1.0 |
| 5.6654048859735715 | 150.069 | -9.229 | 0.0 |
| 5.518391532113626 | 190.664 | -18.539 | 0.0 |
| 5.1808909048116165 | 96.644 | -8.141 | 1.0 |
| 5.5024384933287225 | 162.932 | -9.214 | 0.0 |
| 4.835980274163295 | 132.337 | -8.451 | 0.0 |
| 5.208845565195298 | 98.113 | -5.906 | 1.0 |
| 5.749365533302135 | 126.926 | -10.405 | 1.0 |
| 5.270335397451411 | 89.038 | -12.409 | 1.0 |
| 5.3323103040479065 | 115.136 | -12.186 | 1.0 |
| 5.603059559058559 | 87.611 | -3.653 | 1.0 |
| 4.685417118960068 | 170.459 | -7.971 | 0.0 |
| 5.183531347846387 | 169.186 | -10.352 | 0.0 |
| 5.451537384872765 | 130.075 | -11.559 | 1.0 |
| 5.651097440317911 | 140.057 | -6.712 | 0.0 |
| 6.220097404575559 | 126.003 | -8.606 | 1.0 |
| 5.3241977125281545 | 119.975 | -8.78 | 1.0 |
| 6.0137759121433225 | 167.028 | -6.487 | 0.0 |
| 5.789961271814079 | 154.029 | -6.91 | 0.0 |
| 5.407202667843807 | 68.717 | -18.321 | 1.0 |
| 5.327120737892431 | 210.644 | -18.716 | 0.0 |
| 5.889200011731275 | 150.889 | -15.231 | 0.0 |
| 5.280624822459618 | 42.746 | -7.608 | 1.0 |
| 6.235206644185335 | 151.983 | -7.189 | 0.0 |
| 5.792115857965825 | 103.388 | -15.795 | 1.0 |
| 5.379891500988771 | 84.622 | -12.995 | 1.0 |
| 5.9774886628260795 | 126.988 | -7.598 | 1.0 |
| 5.423584151189881 | 99.976 | -6.921 | 1.0 |
| 4.786235453981626 | 117.532 | -11.684 | 1.0 |
| 4.699778392193864 | 225.791 | -7.064 | 0.0 |
| 5.3462264760360885 | 109.017 | -12.496 | 1.0 |
| 5.616261695215899 | 90.142 | -8.229 | 1.0 |
| 5.153340899640642 | 113.128 | -18.089 | 1.0 |
| 5.439588170019547 | 116.646 | -12.504 | 1.0 |
| 5.7214417936353135 | 57.869 | -26.897 | 1.0 |
| 5.691482505897934 | 86.938 | -4.483 | 1.0 |
| 5.837693420241385 | 131.439 | -12.377 | 0.0 |
| 5.287249998541103 | 96.353 | -6.473 | 1.0 |
| 5.881794174009387 | 90.194 | -11.13 | 1.0 |
| 5.744361367629669 | 83.102 | -9.696 | 1.0 |
| 5.213121558596865 | 85.042 | -5.138 | 1.0 |
| 5.5118720512729995 | 152.964 | -13.695 | 0.0 |
| 5.193444009986724 | 96.01 | -5.837 | 1.0 |
| 5.68004567263334 | 92.659 | -8.59 | 1.0 |
| 5.706965005029758 | 240.448 | -9.875 | 0.0 |
| 4.709709914892611 | 128.862 | -8.669 | 1.0 |
| 5.952001004474009 | 149.83 | -11.42 | 0.0 |
| 5.470188044318821 | 125.025 | -4.917 | 1.0 |
| 5.091365703710136 | 88.133 | -5.638 | 1.0 |
| 6.204281401152069 | 192.504 | -17.651 | 0.0 |
| 5.249433377977233 | 96.484 | -11.025 | 1.0 |
| 5.477202567495158 | 119.973 | -8.448 | 1.0 |
| 5.510816547019034 | 152.932 | -7.368 | 0.0 |
| 5.115646928902234 | 114.75 | -18.492 | 1.0 |
| 5.3942338170785735 | 187.495 | -9.851 | 0.0 |
| 5.633317520186575 | 110.077 | -10.66 | 1.0 |
| 5.489792278278364 | 85.153 | -25.092 | 1.0 |
| 5.118465138450936 | 131.871 | -15.363 | 0.0 |
| 5.260753028847223 | 111.351 | -15.393 | 1.0 |
| 5.750363374801231 | 95.215 | -16.021 | 1.0 |
| 4.812691543803788 | 175.96 | -18.899 | 0.0 |
| 5.426575743730215 | 148.707 | -6.814 | 0.0 |
| 4.857927683654449 | 197.114 | -4.105 | 0.0 |
| 5.460017718602695 | 147.007 | -6.551 | 0.0 |
| 5.199806118788114 | 61.546 | -16.826 | 1.0 |
| 5.641227868701281 | 72.857 | -13.733 | 1.0 |
| 5.579281348076546 | 122.06 | -4.2 | 1.0 |
| 5.812082615379408 | 137.528 | -6.381 | 0.0 |
| 5.362894590061662 | 141.028 | -5.544 | 0.0 |
| 5.170704027647384 | 122.655 | -14.554 | 1.0 |
| 6.038627435601285 | 128.047 | -14.124 | 1.0 |
| 5.045006188946529 | 165.469 | -3.004 | 0.0 |
| 5.109039953782471 | 122.446 | -12.571 | 1.0 |
| 5.54118813055011 | 141.545 | -9.193 | 0.0 |
| 5.32800868428086 | 122.02 | -10.907 | 1.0 |
| 5.667122570497586 | 109.965 | -6.519 | 1.0 |
| 5.0154641497171895 | 63.39 | -13.107 | 1.0 |
| 5.908401094073712 | 113.884 | -6.852 | 1.0 |
| 6.1138473210629245 | 227.001 | -16.889 | 0.0 |
| 5.806205058816614 | 132.383 | -9.016 | 0.0 |
| 5.518496327804453 | 97.069 | -8.376 | 1.0 |
| 6.194307835325854 | 155.568 | -9.598 | 0.0 |
| 5.221195796527836 | 127.304 | -17.207 | 1.0 |
| 5.0053612775551635 | 155.04 | -2.618 | 0.0 |
| 4.883367293037404 | 70.138 | -6.639 | 1.0 |
| 5.452209527247819 | 110.474 | -14.676 | 1.0 |
| 5.134594536181753 | 130.041 | -4.421 | 1.0 |
| 5.652014857268965 | 97.992 | -6.583 | 1.0 |
| 5.393996478288605 | 162.055 | -8.101 | 0.0 |
| 5.6456673680375005 | 112.985 | -5.804 | 1.0 |
| 4.863794002154297 | 99.352 | -11.506 | 1.0 |
| 5.287249998541103 | 158.2 | -8.463 | 0.0 |
| 5.314092039576155 | 131.16 | -15.566 | 0.0 |
| 5.247373958311608 | 95.111 | -32.969 | 1.0 |
| 4.881189259307304 | 90.14 | -11.667 | 1.0 |
| 5.151073471541047 | 102.162 | -18.458 | 1.0 |
| 5.180744021405061 | 127.574 | -7.625 | 1.0 |
| 4.994801464692383 | 77.26 | -16.736 | 1.0 |
| 4.858536086338262 | 166.138 | -7.604 | 0.0 |
| 5.702092447364245 | 143.01 | -6.304 | 0.0 |
| 5.028207592920682 | 100.727 | -11.252 | 1.0 |
| 5.461128195938892 | 89.263 | -9.161 | 1.0 |
| 5.231721530588861 | 90.344 | -14.002 | 1.0 |
| 5.767502911253849 | 140.005 | -6.703 | 0.0 |
| 5.416065734936851 | 97.98 | -5.921 | 1.0 |
| 4.804592602674369 | 91.036 | -14.6 | 1.0 |
| 4.458226274906785 | 95.227 | -18.186 | 1.0 |
| 5.580267040151416 | 116.036 | -8.844 | 1.0 |
| 5.679242859527788 | 125.603 | -5.693 | 1.0 |
| 5.451201144193524 | 119.571 | -9.395 | 1.0 |
| 5.530890439850692 | 150.044 | -8.832 | 0.0 |
| 5.67360501441859 | 81.371 | -7.085 | 1.0 |
| 5.53749315203344 | 144.56 | -5.434 | 0.0 |
| 5.530372729442885 | 96.897 | -7.041 | 1.0 |
| 6.066457379443151 | 135.941 | -11.443 | 0.0 |
| 5.211840646603853 | 74.093 | -18.627 | 1.0 |
| 5.1150195733903905 | 113.24 | -9.424 | 1.0 |
| 5.163107365848958 | 234.853 | -8.071 | 0.0 |
| 4.766868623974747 | 98.38 | -10.965 | 1.0 |
| 5.801557009381438 | 124.859 | -5.928 | 1.0 |
| 5.827973822040402 | 139.917 | -7.787 | 0.0 |
| 5.623741764202893 | 157.485 | -4.855 | 0.0 |
| 5.126717447034353 | 62.246 | -14.606 | 1.0 |
| 5.457236075522337 | 84.991 | -4.545 | 1.0 |
| 5.915754064697231 | 67.495 | -20.449 | 1.0 |
| 5.417689849474705 | 162.07 | -5.124 | 0.0 |
| 5.083463051887064 | 110.856 | -25.629 | 1.0 |
| 5.2364569095267575 | 108.81 | -10.035 | 1.0 |
| 5.504140990115481 | 175.609 | -4.084 | 0.0 |
| 5.782745645993859 | 88.875 | -4.121 | 1.0 |
| 5.428985520263439 | 90.144 | -10.259 | 1.0 |
| 5.394945586446677 | 143.085 | -6.109 | 0.0 |
| 5.446256782063694 | 131.288 | -11.482 | 0.0 |
| 5.038422236258305 | 140.112 | -10.693 | 0.0 |
| 5.2584449210633695 | 150.139 | -7.971 | 0.0 |
| 5.525909607751162 | 125.031 | -5.774 | 1.0 |
| 5.2307437909554215 | 130.193 | -10.108 | 1.0 |
| 5.900421544937317 | 142.991 | -5.235 | 0.0 |
| 4.85589676374095 | 168.071 | -10.85 | 0.0 |
| 3.940312069854874 | 114.519 | -7.18 | 1.0 |
| 6.056899556904485 | 120.939 | -16.839 | 1.0 |
| 5.24199941706601 | 122.322 | -16.949 | 1.0 |
| 5.3677804141515395 | 219.981 | -3.461 | 0.0 |
| 5.5071134027489 | 147.963 | -3.674 | 0.0 |
| 5.365584723524046 | 99.348 | -17.241 | 1.0 |
| 5.277695744967576 | 133.256 | -8.598 | 0.0 |
| 6.0898709287286925 | 125.985 | -10.758 | 1.0 |
| 5.33709625134031 | 124.072 | -13.618 | 1.0 |
| 5.1786851597280235 | 92.191 | -13.569 | 1.0 |
| 5.022028346102827 | 163.439 | -10.039 | 0.0 |
| 5.025637529034568 | 131.458 | -18.005 | 0.0 |
| 6.120757401055048 | 93.972 | -16.714 | 1.0 |
| 4.174267262695061 | 117.909 | -17.923 | 1.0 |
| 6.207917103557972 | 104.98 | -9.259 | 1.0 |
| 6.734419302670816 | 179.835 | -33.438 | 0.0 |
| 6.0230570324407 | 143.619 | -7.186 | 0.0 |
| 4.6061773785992814 | 107.049 | -17.63 | 1.0 |
| 5.229625217696643 | 190.109 | -6.839 | 0.0 |
| 5.511133291041905 | 154.058 | -5.232 | 0.0 |
| 5.446031432894987 | 115.747 | -15.481 | 1.0 |
| 6.615851454127976 | 120.008 | -6.39 | 1.0 |
| 5.481561789037084 | 115.205 | -14.74 | 1.0 |
| 5.734277214176306 | 158.115 | -16.25 | 0.0 |
| 5.140576650244527 | 134.871 | -3.676 | 0.0 |
| 5.88121099672321 | 62.239 | -11.823 | 1.0 |
| 5.53697888891859 | 196.045 | -8.316 | 0.0 |
| 5.775963777688666 | 135.826 | -5.034 | 0.0 |
| 5.417110114495573 | 169.442 | -9.472 | 0.0 |
| 6.102749314680374 | 169.488 | -12.829 | 0.0 |
| 5.2990658363746 | 96.793 | -18.799 | 1.0 |
| 6.199147882794492 | 132.714 | -7.768 | 0.0 |
| 2.48405211811312 | 0.0 | -16.709 | 1.0 |
| 4.637518395185105 | 31.254 | -22.038 | 1.0 |
| 3.254745545095521 | 100.854 | -27.464 | 1.0 |
| 5.3355873802467535 | 123.523 | -7.205 | 1.0 |
| 5.335335656146118 | 50.508 | -10.438 | 1.0 |
| 5.329402430643633 | 128.658 | -12.724 | 1.0 |
| 4.073717615267807 | 70.739 | -16.055 | 1.0 |
| 4.564887525167932 | 238.254 | -11.284 | 0.0 |
| 4.89477353660241 | 180.719 | -13.166 | 0.0 |
| 5.548740736843553 | 90.054 | -10.803 | 1.0 |
| 5.441174600518283 | 80.089 | -11.813 | 1.0 |
| 5.889344691501272 | 205.972 | -8.967 | 0.0 |
| 5.0738640203268615 | 117.048 | -9.627 | 1.0 |
| 5.2087027312927825 | 120.017 | -5.646 | 1.0 |
| 5.4869842077111874 | 135.867 | -8.298 | 0.0 |
| 5.436294020591746 | 84.346 | -14.941 | 1.0 |
| 6.091644939062009 | 168.511 | -1.296 | 0.0 |
| 5.034518929146014 | 80.42 | -12.062 | 1.0 |
| 5.156356142524949 | 74.39 | -14.029 | 1.0 |
| 5.633037175707662 | 134.043 | -5.698 | 0.0 |
| 6.064091650101735 | 125.986 | -10.405 | 1.0 |
| 5.565778351411341 | 129.431 | -4.206 | 1.0 |
| 5.063346206521704 | 130.154 | -14.534 | 1.0 |
| 6.378928383504359 | 113.901 | -20.389 | 1.0 |
| 5.913426759488363 | 85.337 | -10.002 | 1.0 |
| 5.209416751526102 | 125.021 | -6.648 | 1.0 |
| 4.851618460390181 | 115.002 | -18.162 | 1.0 |
| 5.329908761866149 | 44.442 | -10.975 | 1.0 |
| 5.30076107825091 | 93.591 | -9.302 | 1.0 |
| 5.341984382155066 | 159.084 | -11.812 | 0.0 |
| 5.60527203614895 | 111.63 | -6.631 | 1.0 |
| 3.3920520233982674 | 115.901 | -21.162 | 1.0 |
| 5.134132933221371 | 126.245 | -4.765 | 1.0 |
| 4.668397441542593 | 162.139 | -14.93 | 0.0 |
| 5.465447406018871 | 121.993 | -5.775 | 1.0 |
| 5.73038451516079 | 131.11 | -5.061 | 0.0 |
| 5.276762010119172 | 96.441 | -15.351 | 1.0 |
| 5.610351847385838 | 92.758 | -6.482 | 1.0 |
| 5.495599413383852 | 95.377 | -6.289 | 1.0 |
| 5.323815800718434 | 88.023 | -4.729 | 1.0 |
| 5.589973519371366 | 115.341 | -4.88 | 1.0 |
| 5.233534762291271 | 82.92 | -14.814 | 1.0 |
| 5.54853737950824 | 180.153 | -4.271 | 0.0 |
| 4.890462354894079 | 119.575 | -13.016 | 1.0 |
| 6.111880757359681 | 75.202 | -7.952 | 1.0 |
| 6.136076188918148 | 101.863 | -8.621 | 1.0 |
| 5.282484282181109 | 179.942 | -7.084 | 0.0 |
| 5.5081728208599765 | 103.958 | -6.773 | 1.0 |
| 4.910680806169572 | 85.703 | -8.548 | 1.0 |
| 5.5897783399512715 | 105.667 | -8.454 | 1.0 |
| 5.351565768466701 | 130.078 | -8.6 | 1.0 |
| 5.8151253204408455 | 76.75 | -6.823 | 1.0 |
| 5.226963621104301 | 103.26 | -10.707 | 1.0 |
| 5.770928406043289 | 131.749 | -6.099 | 0.0 |
| 5.163107365848958 | 143.446 | -11.994 | 0.0 |
| 4.691665721621109 | 108.718 | -17.471 | 1.0 |
| 6.137318720189338 | 90.672 | -10.488 | 1.0 |
| 4.723496896048981 | 132.015 | -4.98 | 0.0 |
| 5.982575027145929 | 143.82 | -8.604 | 0.0 |
| 5.386849307727097 | 108.89 | -6.368 | 1.0 |
| 3.8280990755466293 | 120.914 | -13.748 | 1.0 |
| 5.47128733320782 | 166.179 | -8.725 | 0.0 |
| 5.167582972957962 | 111.364 | -14.914 | 1.0 |
| 5.475672360360901 | 94.959 | -9.211 | 1.0 |
| 5.140729553205885 | 70.033 | -7.782 | 1.0 |
| 5.775072248464427 | 141.395 | -5.066 | 0.0 |
| 5.453104986343103 | 110.091 | -9.307 | 1.0 |
| 5.099843744640108 | 243.768 | -11.206 | 0.0 |
| 5.4636776829799265 | 129.083 | -3.262 | 1.0 |
| 5.319221592743638 | 100.675 | -13.386 | 1.0 |
| 5.5779984790130435 | 126.625 | -6.831 | 1.0 |
| 5.208274052424202 | 119.453 | -3.818 | 1.0 |
| 5.673963921015516 | 91.982 | -12.072 | 1.0 |
| 5.4936674325315105 | 178.974 | -15.391 | 0.0 |
| 5.9154722545833565 | 124.998 | -9.686 | 1.0 |
| 5.3992054608226665 | 129.961 | -7.922 | 1.0 |
| 5.859465380026135 | 114.669 | -14.172 | 1.0 |
| 6.044899763442047 | 120.309 | -8.014 | 1.0 |
| 5.960726408973159 | 123.838 | -15.306 | 1.0 |
| 5.440155022703225 | 105.76 | -17.516 | 1.0 |
| 5.745113587553698 | 132.025 | -8.882 | 0.0 |
| 4.727665479276123 | 69.439 | -28.0 | 1.0 |
| 5.671089054964057 | 206.088 | -13.704 | 0.0 |
| 5.437658437760656 | 139.042 | -6.488 | 0.0 |
| 2.7231969083654577 | 0.0 | -6.31 | 1.0 |
| 5.73038451516079 | 92.93 | -4.998 | 1.0 |
| 5.116273891085884 | 157.324 | -12.215 | 0.0 |
| 5.328262259295593 | 106.934 | -6.312 | 1.0 |
| 5.230184660726791 | 70.322 | -21.508 | 1.0 |
| 5.482105347616156 | 133.828 | -7.421 | 0.0 |
| 5.486443295335265 | 102.293 | -9.785 | 1.0 |
| 6.341764136593634 | 85.02 | -6.875 | 1.0 |
| 5.502225497673697 | 148.998 | -10.129 | 0.0 |
| 5.827973822040402 | 130.031 | -9.11 | 1.0 |
| 5.484601976728484 | 193.718 | -7.747 | 0.0 |
| 5.527884164573581 | 91.034 | -6.192 | 1.0 |
| 5.172927427877826 | 185.86 | -24.732 | 0.0 |
| 5.2885697626443715 | 127.053 | -5.689 | 1.0 |
| 5.381815742669535 | 130.003 | -6.399 | 1.0 |
| 5.483191579208695 | 107.992 | -13.702 | 1.0 |
| 5.105720054556155 | 187.591 | -13.536 | 0.0 |
| 5.4750158098978945 | 159.666 | -4.36 | 0.0 |
| 5.5472144309132965 | 80.345 | -11.443 | 1.0 |
| 5.56427781084874 | 168.975 | -7.891 | 0.0 |
| 6.1470359886037995 | 82.436 | -12.468 | 1.0 |
| 5.279427623446258 | 84.67 | -5.292 | 1.0 |
| 5.821724993890699 | 83.845 | -13.803 | 1.0 |
| 5.737986555009911 | 133.735 | -10.051 | 0.0 |
| 5.352184762591424 | 116.218 | -7.483 | 1.0 |
| 5.811770016656704 | 110.202 | -9.089 | 1.0 |
| 4.267090277755128 | 107.749 | -3.361 | 1.0 |
| 5.882595494838628 | 82.998 | -16.438 | 1.0 |
| 5.997096507802042 | 237.544 | -14.794 | 0.0 |
| 5.248472811704074 | 126.215 | -15.351 | 1.0 |
| 5.0266663735946135 | 99.977 | -3.827 | 1.0 |
| 5.87550713773137 | 104.225 | -7.841 | 1.0 |
| 4.071492770946719 | 89.232 | -24.717 | 1.0 |
| 5.754758628760087 | 130.036 | -4.706 | 1.0 |
| 5.459350805751235 | 159.978 | -3.232 | 0.0 |
| 5.871023641818345 | 107.863 | -15.638 | 1.0 |
| 5.222746532625901 | 131.978 | -5.67 | 0.0 |
| 6.341626102410307 | 123.993 | -8.585 | 1.0 |
| 4.786453370526792 | 130.029 | -9.234 | 1.0 |
| 5.658686742660347 | 125.107 | -11.981 | 1.0 |
| 4.780333181311705 | 181.15 | -7.41 | 0.0 |
| 5.453664230661525 | 130.128 | -4.531 | 1.0 |
| 5.143783004790946 | 93.053 | -10.299 | 1.0 |
| 5.569768756207467 | 165.932 | -6.124 | 0.0 |
| 5.124544227693321 | 146.426 | -10.195 | 0.0 |
| 6.000661797437479 | 129.007 | -7.181 | 1.0 |
| 5.517867348720116 | 191.787 | -12.92 | 0.0 |
| 4.370691635610005 | 89.638 | -6.059 | 1.0 |
| 5.525285235827343 | 156.019 | -5.927 | 0.0 |
| 5.755337701403143 | 131.966 | -7.267 | 0.0 |
| 5.53687601242964 | 130.494 | -4.506 | 1.0 |
| 5.271677659599143 | 112.984 | -5.779 | 1.0 |
| 5.094413293795442 | 45.955 | -12.685 | 1.0 |
| 5.41745801356627 | 108.017 | -6.207 | 1.0 |
| 5.398023958861762 | 125.085 | -12.094 | 1.0 |
| 5.094893657240654 | 62.415 | -16.983 | 1.0 |
| 5.130586525664302 | 116.063 | -10.601 | 1.0 |
| 5.22822526613027 | 110.102 | -16.477 | 1.0 |
| 5.286589462747781 | 96.469 | -7.544 | 1.0 |
| 5.521635422635403 | 75.186 | -5.503 | 1.0 |
| 5.025980616731968 | 114.348 | -2.548 | 1.0 |
| 5.51943911539636 | 94.183 | -5.847 | 1.0 |
| 5.697458956625815 | 99.988 | -10.737 | 1.0 |
| 5.86503890587163 | 91.139 | -9.13 | 1.0 |
| 5.489360777153861 | 100.992 | -3.3 | 1.0 |
| 5.3578617345120865 | 121.875 | -6.827 | 1.0 |
| 5.769217125399671 | 72.299 | -14.883 | 1.0 |
| 5.046015307863599 | 109.808 | -12.783 | 1.0 |
| 5.274223066183203 | 111.052 | -9.008 | 1.0 |
| 5.9798699133794955 | 131.997 | -3.879 | 0.0 |
| 5.580858019525172 | 128.0 | -4.988 | 1.0 |
| 5.82928035569681 | 97.345 | -11.499 | 1.0 |
| 6.094712435238956 | 126.965 | -7.269 | 1.0 |
| 5.679867340584397 | 140.047 | -5.647 | 0.0 |
| 5.404504900132244 | 152.579 | -12.128 | 0.0 |
| 5.726391454576292 | 172.19 | -10.391 | 0.0 |
| 6.26857580737286 | 127.984 | -8.235 | 1.0 |
| 5.527987970235486 | 159.966 | -13.347 | 0.0 |
| 5.215820203328058 | 121.171 | -9.677 | 1.0 |
| 5.172038638226674 | 171.333 | -12.577 | 0.0 |
| 5.114391824056528 | 118.825 | -17.069 | 1.0 |
| 5.443210556309651 | 90.877 | -20.028 | 1.0 |
| 6.341764136593634 | 125.005 | -11.208 | 1.0 |
| 5.410010026795568 | 104.63 | -5.123 | 1.0 |
| 5.157258916570002 | 94.011 | -11.14 | 1.0 |
| 5.3335720040567285 | 197.822 | -8.291 | 0.0 |
| 5.722467876781503 | 133.342 | -15.668 | 0.0 |
| 4.906051143655614 | 150.871 | -12.438 | 0.0 |
| 5.639279330670629 | 128.078 | -5.433 | 1.0 |
| 3.9398041119997855 | 74.721 | -22.394 | 1.0 |
| 5.784433950838736 | 95.991 | -16.756 | 1.0 |
| 5.419195651418814 | 119.999 | -3.964 | 1.0 |
| 5.365584723524046 | 105.987 | -3.815 | 1.0 |
| 5.224294867664447 | 184.893 | -5.982 | 0.0 |
| 5.216670936098296 | 202.14 | -4.63 | 0.0 |
| 5.503715637575613 | 117.547 | -11.625 | 1.0 |
| 5.423814571344231 | 144.818 | -8.575 | 0.0 |
| 5.573346590758997 | 81.476 | -15.363 | 1.0 |
| 5.3717933140162275 | 151.128 | -6.697 | 0.0 |
| 7.0423371496613365 | 77.561 | -15.204 | 1.0 |
| 5.65027103779804 | 114.308 | -6.593 | 1.0 |
| 6.1900333988798035 | 114.924 | -10.508 | 1.0 |
| 5.15981242631456 | 112.042 | -9.297 | 1.0 |
| 5.367292904551431 | 135.027 | -7.629 | 0.0 |
| 5.212125453705929 | 126.01 | -2.728 | 1.0 |
| 5.801951745180824 | 104.385 | -22.224 | 1.0 |
| 3.8993628021851645 | 56.245 | -10.257 | 1.0 |
| 6.025332353214288 | 135.958 | -8.944 | 0.0 |
| 4.711120737540498 | 135.419 | -23.929 | 0.0 |
| 5.3176854876953845 | 113.557 | -7.572 | 1.0 |
| 4.88257583642476 | 104.714 | -9.823 | 1.0 |
| 5.288042055894653 | 142.744 | -8.189 | 0.0 |
| 5.726817030503055 | 80.231 | -13.916 | 1.0 |
| 5.568373930809033 | 105.635 | -11.253 | 1.0 |
| 5.574139929699739 | 94.151 | -10.945 | 1.0 |
| 5.314220636516537 | 116.358 | -10.622 | 1.0 |
| 5.185872594474605 | 81.181 | -7.193 | 1.0 |
| 5.263191047910029 | 92.266 | -15.062 | 1.0 |
| 5.5339908888778595 | 108.004 | -9.795 | 1.0 |
| 5.056715949590252 | 165.439 | -9.06 | 0.0 |
| 3.9408197698197713 | 122.261 | -18.636 | 1.0 |
| 6.203859013449871 | 197.914 | -11.021 | 0.0 |
| 5.865335290950203 | 132.092 | -12.164 | 0.0 |
| 5.2981518028431225 | 84.141 | -4.425 | 1.0 |
| 5.317301129254338 | 93.069 | -10.056 | 1.0 |
| 5.531924978120003 | 140.965 | -10.094 | 0.0 |
| 5.374096459458477 | 151.936 | -6.81 | 0.0 |
| 5.945936444126941 | 134.409 | -7.102 | 0.0 |
| 7.211785910002847 | 96.83 | -18.114 | 1.0 |
| 5.3500785889402005 | 147.991 | -11.104 | 0.0 |
| 5.027522892507361 | 120.527 | -7.075 | 1.0 |
| 5.465115810845681 | 124.005 | -7.957 | 1.0 |
| 4.956938481993811 | 119.446 | -19.653 | 1.0 |
| 4.6649585052598415 | 163.919 | -4.968 | 0.0 |
| 5.528403124883772 | 104.907 | -13.77 | 1.0 |
| 5.469527924173878 | 103.983 | -8.127 | 1.0 |
| 5.648984132478454 | 163.031 | -4.385 | 0.0 |
| 5.45679029199923 | 155.982 | -6.263 | 0.0 |
| 5.030941716035228 | 111.224 | -3.903 | 1.0 |
| 5.270066697826635 | 140.063 | -4.937 | 0.0 |
| 5.217520891529926 | 94.307 | -8.907 | 1.0 |
| 5.622514716915152 | 101.809 | -11.666 | 1.0 |
| 6.5302317923969815 | 123.571 | -12.814 | 1.0 |
| 4.828068427216242 | 231.953 | -8.168 | 0.0 |
| 6.153610203197906 | 121.026 | -10.18 | 1.0 |
| 5.584003924284049 | 100.083 | -7.944 | 1.0 |
| 5.2237321189780594 | 160.038 | -10.534 | 0.0 |
| 5.494204452408169 | 105.99 | -5.787 | 1.0 |
| 5.219218692301358 | 94.268 | -7.142 | 1.0 |
| 5.515031936175296 | 87.356 | -10.521 | 1.0 |
| 4.612680214992582 | 152.43 | -12.941 | 0.0 |
| 5.173519496873623 | 136.255 | -9.675 | 0.0 |
| 4.976229408368982 | 143.87 | -15.836 | 0.0 |
| 5.61910883575362 | 143.004 | -5.542 | 0.0 |
| 5.925916423159631 | 120.003 | -12.754 | 1.0 |
| 5.413158924742671 | 93.787 | -9.833 | 1.0 |
| 4.731816845633907 | 185.055 | -13.618 | 0.0 |
| 5.286721594699027 | 84.843 | -6.906 | 1.0 |
| 5.995536744446201 | 90.218 | -16.787 | 1.0 |
| 5.5118720512729995 | 154.237 | -7.34 | 0.0 |
| 5.339229916967983 | 98.959 | -3.163 | 1.0 |
| 5.303493497240193 | 135.886 | -6.352 | 0.0 |
| 5.449181408435859 | 95.017 | -4.009 | 1.0 |
| 5.344731328832199 | 152.703 | -11.977 | 0.0 |
| 4.904696833682751 | 104.281 | -13.585 | 1.0 |
| 5.809422421269561 | 210.415 | -10.595 | 0.0 |
| 5.136746105517679 | 132.335 | -4.782 | 0.0 |
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| 5.002556212696598 | 162.564 | -7.579 | 0.0 |
| 5.408841273925989 | 119.996 | -5.901 | 1.0 |
| 5.381335029266696 | 103.008 | -6.436 | 1.0 |
| 5.460906216101348 | 138.07 | -14.387 | 0.0 |
| 4.42843730342153 | 114.345 | -4.951 | 1.0 |
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| 5.226261971032257 | 103.479 | -16.027 | 1.0 |
| 5.307125163770039 | 107.578 | -16.041 | 1.0 |
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| 5.599973125687407 | 78.979 | -6.21 | 1.0 |
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| 5.568971940469297 | 91.041 | -6.672 | 1.0 |
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| 5.245034726302448 | 113.111 | -8.918 | 1.0 |
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| 5.479275577741912 | 119.922 | -8.373 | 1.0 |
| 5.865113031652334 | 86.034 | -16.263 | 1.0 |
| 5.032135499299219 | 67.825 | -9.567 | 1.0 |
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| 4.38901689577076 | 126.156 | -7.754 | 1.0 |
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| 5.249296225705547 | 165.012 | -5.835 | 0.0 |
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| 5.600842139109561 | 191.836 | -8.413 | 0.0 |
| 5.9780183254931805 | 116.221 | -10.097 | 1.0 |
| 5.629291839552761 | 88.028 | -5.63 | 1.0 |
| 5.45556335097355 | 133.997 | -4.746 | 0.0 |
| 5.453328704474266 | 99.637 | -12.155 | 1.0 |
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| 4.7148731097299965 | 175.881 | -3.677 | 0.0 |
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| 5.249707678591793 | 139.951 | -13.486 | 0.0 |
| 5.683161652730066 | 70.441 | -14.599 | 1.0 |
| 5.602288843325435 | 103.795 | -11.676 | 1.0 |
| 4.510971148636285 | 179.085 | -8.22 | 0.0 |
| 5.419427084876769 | 159.842 | -14.056 | 0.0 |
| 5.7238343519327755 | 117.917 | -11.966 | 1.0 |
| 5.149407413424839 | 116.302 | -6.262 | 1.0 |
| 5.358353862511185 | 53.788 | -10.868 | 1.0 |
| 5.049874019907821 | 197.29 | -23.707 | 0.0 |
| 4.589865463932764 | 158.84 | -7.684 | 0.0 |
| 5.364240537989767 | 100.075 | -4.86 | 1.0 |
| 5.106827932903924 | 150.064 | -14.802 | 0.0 |
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| 5.505097362174365 | 127.985 | -4.444 | 1.0 |
| 5.143478108038215 | 87.533 | -7.045 | 1.0 |
| 4.2601062865062 | 74.36 | -12.725 | 1.0 |
| 5.199085200801644 | 147.04 | -8.922 | 0.0 |
| 5.584592662453535 | 99.268 | -12.358 | 1.0 |
| 5.679421302966559 | 81.362 | -7.025 | 1.0 |
| 5.333824124190991 | 79.702 | -17.71 | 1.0 |
| 5.331931526930796 | 136.835 | -12.374 | 0.0 |
| 5.375427470647804 | 141.721 | -13.633 | 0.0 |
| 5.337221875868001 | 146.362 | -8.55 | 0.0 |
| 5.653572558115166 | 106.373 | -7.068 | 1.0 |
| 5.384934736760523 | 163.979 | -1.952 | 0.0 |
| 5.226683009407633 | 217.213 | -17.298 | 0.0 |
| 5.061858242676265 | 89.004 | -13.367 | 1.0 |
| 5.417689849474705 | 143.676 | -6.858 | 0.0 |
| 5.356630330305323 | 125.111 | -8.183 | 1.0 |
| 5.162060147947414 | 103.718 | -15.488 | 1.0 |
| 6.218537428483649 | 125.008 | -7.708 | 1.0 |
| 5.101435379302713 | 125.069 | -9.807 | 1.0 |
| 5.48784907594078 | 104.492 | -7.694 | 1.0 |
| 5.78250424874252 | 130.039 | -5.188 | 1.0 |
| 5.088147664703838 | 183.836 | -4.671 | 0.0 |
| 6.3148595771487095 | 120.373 | -8.901 | 1.0 |
| 5.451537384872765 | 168.477 | -7.374 | 0.0 |
| 5.706183523164159 | 99.927 | -9.566 | 1.0 |
| 5.502332001172103 | 95.333 | -6.493 | 1.0 |
| 5.521739878948984 | 169.369 | -12.558 | 0.0 |
| 4.711120737540498 | 138.238 | -8.843 | 0.0 |
| 5.609491062398614 | 87.041 | -6.835 | 1.0 |
| 5.180009168017768 | 126.865 | -6.458 | 1.0 |
| 5.891223432702917 | 207.899 | -10.47 | 0.0 |
| 5.5207992990407755 | 67.511 | -8.083 | 1.0 |
| 5.890428998615078 | 0.0 | -10.695 | 1.0 |
| 5.883323391273292 | 73.209 | -20.003 | 1.0 |
| 3.584866086200537 | 97.91 | -13.766 | 1.0 |
| 5.08927517211037 | 86.478 | -6.99 | 1.0 |
| 5.812551315369655 | 179.721 | -14.285 | 0.0 |
| 5.458127046842273 | 110.03 | -5.795 | 1.0 |
| 5.648616145884805 | 94.383 | -7.25 | 1.0 |
| 5.425196110546038 | 167.907 | -8.459 | 0.0 |
| 5.219642706004484 | 62.586 | -20.636 | 1.0 |
| 6.295629588470401 | 126.038 | -12.04 | 1.0 |
| 5.711554758718933 | 132.723 | -7.209 | 0.0 |
| 5.389952711501166 | 127.897 | -6.24 | 1.0 |
| 5.026323521131626 | 149.006 | -5.661 | 0.0 |
| 6.186009468267858 | 131.985 | -4.922 | 0.0 |
| 5.7577331101832545 | 125.008 | -7.131 | 1.0 |
| 4.9099106826762755 | 79.832 | -11.845 | 1.0 |
| 5.731570843290727 | 142.118 | -5.631 | 0.0 |
| 5.1643027714857315 | 96.796 | -8.626 | 1.0 |
| 5.063676599821411 | 207.965 | -5.222 | 0.0 |
| 5.143325566411583 | 105.504 | -7.655 | 1.0 |
| 5.201965759646714 | 96.005 | -4.967 | 1.0 |
| 5.519648476382033 | 98.964 | -7.278 | 1.0 |
| 5.030600326504665 | 79.426 | -10.867 | 1.0 |
| 5.140576650244527 | 165.52 | -14.734 | 0.0 |
| 5.311001679436338 | 89.441 | -6.668 | 1.0 |
| 5.634624696715785 | 133.31 | -4.637 | 0.0 |
| 5.364729537556912 | 160.023 | -10.053 | 0.0 |
| 5.8619215616314975 | 175.06 | -7.078 | 0.0 |
| 5.452433445776701 | 114.964 | -7.285 | 1.0 |
| 4.986450997291523 | 93.5 | -3.815 | 1.0 |
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| 5.363139425609932 | 101.752 | -12.863 | 1.0 |
| 5.330288354217008 | 95.627 | -15.966 | 1.0 |
| 5.229625217696643 | 179.952 | -13.267 | 0.0 |
| 5.29278142144123 | 151.535 | -5.267 | 0.0 |
| 5.809892369459324 | 132.981 | -10.682 | 0.0 |
| 5.849504590869244 | 220.039 | -5.11 | 0.0 |
| 5.519962475758416 | 160.106 | -5.807 | 0.0 |
| 5.023232876959716 | 80.384 | -14.562 | 1.0 |
| 5.827512296778853 | 142.155 | -6.301 | 0.0 |
| 4.476217893264733 | 176.354 | -5.243 | 0.0 |
| 4.716977625501516 | 121.601 | -11.847 | 1.0 |
| 4.187047102663966 | 87.83 | -13.689 | 1.0 |
| 5.377842940530993 | 119.762 | -4.769 | 1.0 |
| 4.952703169254423 | 110.686 | -11.218 | 1.0 |
| 5.529440238242905 | 90.929 | -14.502 | 1.0 |
| 5.363261820908567 | 122.936 | -9.6 | 1.0 |
| 5.156205552886701 | 62.968 | -8.561 | 1.0 |
| 4.929365873206521 | 102.929 | -12.912 | 1.0 |
| 5.105720054556155 | 86.232 | -7.316 | 1.0 |
| 5.551786455213501 | 128.896 | -15.767 | 1.0 |
| 5.492700061177452 | 98.054 | -6.596 | 1.0 |
| 4.682278160773387 | 127.932 | -10.676 | 1.0 |
| 5.910244404863671 | 111.747 | -7.658 | 1.0 |
| 5.308419022584635 | 92.062 | -7.396 | 1.0 |
| 4.966084411461676 | 135.534 | -10.839 | 0.0 |
| 4.973883477375123 | 115.076 | -25.03 | 1.0 |
| 6.175525132581313 | 202.045 | -11.959 | 0.0 |
| 5.113449430480435 | 112.603 | -16.584 | 1.0 |
| 5.288305969330356 | 112.565 | -11.702 | 1.0 |
| 5.983102003303539 | 159.789 | -11.868 | 0.0 |
| 5.567176799358838 | 145.129 | -4.733 | 0.0 |
| 5.58095646957768 | 104.256 | -15.552 | 1.0 |
| 5.478948537110532 | 105.101 | -9.257 | 1.0 |
| 5.641598583644489 | 97.622 | -13.01 | 1.0 |
| 5.822421232966385 | 162.531 | -13.611 | 0.0 |
| 5.353668815476439 | 71.494 | -11.247 | 1.0 |
| 5.275159224804866 | 127.005 | -5.155 | 1.0 |
| 5.217095977001863 | 203.536 | -5.012 | 0.0 |
| 5.248884603456135 | 138.029 | -4.596 | 0.0 |
| 5.428412298800484 | 101.978 | -5.839 | 1.0 |
| 6.1580428262859765 | 97.999 | -10.201 | 1.0 |
| 5.700346511824177 | 100.664 | -8.013 | 1.0 |
| 5.376998178031043 | 132.682 | -12.438 | 0.0 |
| 5.494741225132456 | 131.49 | -11.232 | 0.0 |
| 5.732586590899394 | 201.81 | -4.167 | 0.0 |
| 5.547723458446129 | 140.081 | -7.26 | 0.0 |
| 5.090562179678058 | 163.105 | -6.223 | 0.0 |
| 5.362649647669598 | 110.489 | -6.202 | 1.0 |
| 5.377239631375239 | 106.01 | -5.466 | 1.0 |
| 5.5502647555781275 | 85.61 | -5.781 | 1.0 |
| 5.362527177416307 | 135.122 | -8.42 | 0.0 |
| 5.102706841247405 | 122.77 | -7.029 | 1.0 |
| 5.680402275474472 | 118.03 | -5.798 | 1.0 |
| 5.546908874376848 | 189.986 | -4.537 | 0.0 |
| 5.5721554009981205 | 110.946 | -12.825 | 1.0 |
| 5.2564039954429544 | 99.836 | -15.637 | 1.0 |
| 5.215536501039555 | 87.731 | -17.643 | 1.0 |
| 6.448692317766558 | 124.792 | -10.583 | 1.0 |
| 5.463899048527427 | 142.86 | -5.746 | 0.0 |
| 2.148000485700547 | 0.0 | -8.435 | 1.0 |
| 6.320232899153641 | 126.024 | -12.36 | 1.0 |
| 5.606040456996324 | 147.692 | -7.499 | 0.0 |
| 5.400975032797074 | 162.898 | -2.856 | 0.0 |
| 5.239509116005083 | 134.153 | -17.027 | 0.0 |
| 5.577405806789707 | 115.884 | -5.74 | 1.0 |
| 4.549264269798323 | 121.576 | -21.839 | 1.0 |
| 3.6527962139408547 | 41.35 | -23.844 | 1.0 |
| 5.214968800541323 | 111.461 | -9.788 | 1.0 |
| 5.28513470584072 | 172.75 | -10.357 | 0.0 |
| 5.19460377174902 | 171.625 | -17.449 | 0.0 |
| 4.398055114513321 | 96.895 | -6.086 | 1.0 |
| 5.471507020818305 | 159.737 | -7.027 | 0.0 |
| 4.83078212831702 | 133.981 | -15.796 | 0.0 |
| 5.481779231277037 | 132.01 | -2.852 | 0.0 |
| 5.813175927081176 | 82.216 | -15.964 | 1.0 |
| 5.441401003374007 | 87.026 | -5.83 | 1.0 |
| 5.325596776831915 | 198.851 | -4.724 | 0.0 |
| 5.427609209970511 | 85.625 | -17.708 | 1.0 |
| 5.474796891858358 | 175.59 | -5.836 | 0.0 |
| 5.5875312401931 | 116.656 | -6.006 | 1.0 |
| 6.291380825532051 | 124.024 | -7.941 | 1.0 |
| 4.753670705106405 | 163.511 | -5.599 | 0.0 |
| 5.235900965453425 | 89.44 | -11.965 | 1.0 |
| 5.52194879882897 | 120.01 | -4.914 | 1.0 |
| 6.214261018196818 | 126.976 | -5.224 | 1.0 |
| 5.265218234227128 | 131.675 | -6.413 | 0.0 |
| 5.485902090214787 | 91.996 | -2.446 | 1.0 |
| 5.955595086230608 | 126.687 | -10.763 | 1.0 |
| 5.605368148637933 | 120.034 | -8.096 | 1.0 |
| 5.7297908066738845 | 120.002 | -11.244 | 1.0 |
| 6.8447011063207555 | 115.593 | -12.89 | 1.0 |
| 5.059705038227187 | 97.891 | -10.768 | 1.0 |
| 5.641876503095035 | 139.593 | -9.691 | 0.0 |
| 5.070753058070189 | 114.43 | -12.898 | 1.0 |
| 5.264002384594248 | 140.596 | -10.308 | 0.0 |
| 7.319039310933789 | 60.94 | -21.661 | 1.0 |
| 5.242413882601138 | 56.142 | -17.18 | 1.0 |
| 5.871465548643589 | 76.905 | -7.595 | 1.0 |
| 5.3323103040479065 | 185.577 | -5.884 | 0.0 |
| 5.462237483923419 | 125.022 | -5.941 | 1.0 |
| 5.562775015280007 | 103.544 | -15.525 | 1.0 |
| 5.976096943041082 | 165.33 | -7.812 | 0.0 |
| 5.054384966781395 | 81.619 | -16.783 | 1.0 |
| 5.509336918302029 | 172.147 | -16.273 | 0.0 |
| 5.243656196703 | 154.542 | -5.113 | 0.0 |
| 5.208274052424202 | 163.974 | -6.227 | 0.0 |
| 5.528403124883772 | 93.04 | -5.971 | 1.0 |
| 5.607000171810274 | 111.365 | -10.116 | 1.0 |
| 5.237845445572235 | 95.722 | -10.6 | 1.0 |
| 4.898481981767724 | 96.288 | -5.465 | 1.0 |
| 5.10714420967513 | 131.144 | -10.51 | 0.0 |
| 5.731740222431003 | 103.682 | -12.932 | 1.0 |
| 6.153832302679555 | 139.983 | -5.243 | 0.0 |
| 6.140755810501547 | 127.983 | -9.151 | 1.0 |
| 5.816526554590968 | 89.275 | -3.902 | 1.0 |
| 5.466772646002193 | 124.169 | -4.783 | 1.0 |
| 5.532235146841884 | 160.071 | -10.728 | 0.0 |
| 4.714170589210512 | 216.105 | -5.759 | 0.0 |
| 5.26710653645963 | 91.057 | -16.497 | 1.0 |
| 5.344357180540937 | 189.847 | -5.161 | 0.0 |
| 4.946409115285852 | 78.587 | -16.969 | 1.0 |
| 5.6151206090866115 | 113.798 | -23.995 | 1.0 |
| 5.472714566733009 | 119.944 | -4.863 | 1.0 |
| 2.416456488150271 | 85.918 | -29.744 | 1.0 |
| 5.95281586631299 | 137.976 | -7.193 | 0.0 |
| 5.224997845023872 | 101.607 | -15.219 | 1.0 |
| 5.741933673775497 | 89.962 | -21.141 | 1.0 |
| 5.032987413771107 | 149.967 | -17.245 | 0.0 |
| 5.156657138568294 | 125.669 | -9.773 | 1.0 |
| 5.096173397462181 | 116.699 | -16.736 | 1.0 |
| 5.941827117860842 | 129.44 | -8.481 | 1.0 |
| 5.75326807949039 | 100.08 | -6.938 | 1.0 |
| 5.239370542691106 | 160.193 | -12.146 | 0.0 |
| 5.487308631306926 | 93.941 | -8.156 | 1.0 |
| 5.497527627913457 | 102.915 | -7.999 | 1.0 |
| 4.687343913732275 | 96.17 | -3.083 | 1.0 |
| 5.740927380966297 | 96.192 | -18.309 | 1.0 |
| 5.027865268551932 | 113.231 | -18.821 | 1.0 |
| 5.057214762509251 | 193.52 | -7.094 | 0.0 |
| 5.043490663158142 | 120.9 | -11.805 | 1.0 |
| 5.545175686665518 | 177.047 | -7.456 | 0.0 |
| 5.4739206562390805 | 102.869 | -5.441 | 1.0 |
| 3.7525906018224884 | 114.055 | -3.771 | 1.0 |
| 5.250256001739822 | 98.65 | -6.818 | 1.0 |
| 5.488929089755756 | 125.022 | -9.259 | 1.0 |
| 6.2047563691077325 | 65.068 | -11.986 | 1.0 |
| 5.564978348428669 | 133.972 | -5.126 | 0.0 |
| 5.766522034883475 | 196.016 | -6.212 | 0.0 |
| 5.715607384022325 | 135.248 | -7.133 | 0.0 |
| 5.988618551100562 | 126.002 | -9.284 | 1.0 |
| 5.2432422458510715 | 112.895 | -5.149 | 1.0 |
| 5.98092644079866 | 135.062 | -5.471 | 0.0 |
| 5.96971012630147 | 85.231 | -11.807 | 1.0 |
| 5.695529310800033 | 125.142 | -6.341 | 1.0 |
| 5.574437279097848 | 127.022 | -10.96 | 1.0 |
| 5.736639295234792 | 94.001 | -8.066 | 1.0 |
| 5.252856399447034 | 138.201 | -4.53 | 0.0 |
| 4.842800613938594 | 148.436 | -4.54 | 0.0 |
| 5.326993837902569 | 124.389 | -7.918 | 1.0 |
| 5.309323674314746 | 135.08 | -7.997 | 0.0 |
| 5.309452885876903 | 212.408 | -14.007 | 0.0 |
| 5.237151391976638 | 114.878 | -8.252 | 1.0 |
| 5.638536076366039 | 151.461 | -12.952 | 0.0 |
| 5.488173219119607 | 171.439 | -13.77 | 0.0 |
| 5.327501389830855 | 91.117 | -6.481 | 1.0 |
| 5.296713831572754 | 100.017 | -9.108 | 1.0 |
| 5.944979097542342 | 113.891 | -19.76 | 1.0 |
| 5.990385289919405 | 92.99 | -5.934 | 1.0 |
| 5.396604338579462 | 93.991 | -5.766 | 1.0 |
| 4.864197272882948 | 182.695 | -14.1 | 0.0 |
| 5.262243612520556 | 138.059 | -7.885 | 0.0 |
| 5.664771339539909 | 87.545 | -9.949 | 1.0 |
| 5.351070287662016 | 69.291 | -19.93 | 1.0 |
| 6.131885446236486 | 164.968 | -7.435 | 0.0 |
| 5.186164893232419 | 43.871 | -19.915 | 1.0 |
| 5.440834856737833 | 150.118 | -4.63 | 0.0 |
| 5.385174281609037 | 99.982 | -6.635 | 1.0 |
| 6.019824791560191 | 129.05 | -5.429 | 1.0 |
| 5.502970824816003 | 129.989 | -4.38 | 1.0 |
| 5.462902432614862 | 180.366 | -3.918 | 0.0 |
| 5.508066937648978 | 123.614 | -9.07 | 1.0 |
| 5.512821061562089 | 84.017 | -6.863 | 1.0 |
| 4.63168445542683 | 102.168 | -24.321 | 1.0 |
| 6.125851723445143 | 109.562 | -10.705 | 1.0 |
| 5.507219386966034 | 161.141 | -15.025 | 0.0 |
| 5.64815598094485 | 173.833 | -5.775 | 0.0 |
| 5.086211858768461 | 186.861 | -3.525 | 0.0 |
| 5.902993350554627 | 127.192 | -9.154 | 1.0 |
| 6.076104731708314 | 127.374 | -9.181 | 1.0 |
| 5.9366666186584505 | 112.741 | -11.406 | 1.0 |
| 6.154109852370004 | 128.497 | -11.879 | 1.0 |
| 5.460239895734497 | 90.061 | -9.326 | 1.0 |
| 3.461631553971685 | 30.044 | -18.309 | 1.0 |
| 5.696055968604025 | 141.989 | -6.872 | 0.0 |
| 6.272281433001033 | 95.314 | -15.598 | 1.0 |
| 5.576812820943777 | 85.975 | -3.637 | 1.0 |
| 5.321138423993106 | 61.559 | -25.038 | 1.0 |
| 5.40026758875988 | 152.803 | -4.389 | 0.0 |
| 4.922922798653241 | 96.243 | -10.633 | 1.0 |
| 5.43047442145297 | 147.8 | -8.369 | 0.0 |
| 5.2307437909554215 | 184.033 | -8.551 | 0.0 |
| 5.414322645152568 | 125.988 | -7.567 | 1.0 |
| 6.26401140791294 | 93.605 | -12.174 | 1.0 |
| 5.301412322227913 | 134.362 | -4.411 | 0.0 |
| 5.261701824986476 | 125.159 | -21.073 | 1.0 |
| 5.601228129128594 | 104.393 | -16.924 | 1.0 |
| 5.1183087927453625 | 93.306 | -13.555 | 1.0 |
| 5.415717351071923 | 96.073 | -7.381 | 1.0 |
| 5.739836062373653 | 100.074 | -7.242 | 1.0 |
| 5.619677270652041 | 90.005 | -9.912 | 1.0 |
| 5.207273104763058 | 171.76 | -7.701 | 0.0 |
| 5.574833594834567 | 101.021 | -3.807 | 1.0 |
| 5.013555891662754 | 107.696 | -5.334 | 1.0 |
| 4.077709632470248 | 123.256 | -3.013 | 1.0 |
| 4.852026748288437 | 139.759 | -8.03 | 0.0 |
| 5.303233611985233 | 125.085 | -8.231 | 1.0 |
| 5.789561773504737 | 131.846 | -13.308 | 0.0 |
| 5.439361356314339 | 163.879 | -11.664 | 0.0 |
| 5.353421649883508 | 131.86 | -8.159 | 0.0 |
| 6.152498965263847 | 219.988 | -9.408 | 0.0 |
| 5.541290564401247 | 242.155 | -4.502 | 0.0 |
| 5.502118982831087 | 162.584 | -4.428 | 0.0 |
| 6.022740611873921 | 90.211 | -20.61 | 1.0 |
| 4.6964458745301325 | 153.22 | -5.736 | 0.0 |
| 5.592896350533049 | 105.241 | -5.748 | 1.0 |
| 5.105244864895034 | 197.736 | -14.357 | 0.0 |
| 6.104850529880597 | 126.968 | -13.554 | 1.0 |
| 5.781296264233327 | 65.017 | -8.469 | 1.0 |
| 5.003784346522015 | 115.874 | -10.524 | 1.0 |
| 5.4597954495529875 | 93.984 | -4.948 | 1.0 |
| 5.282218878449877 | 105.617 | -28.85 | 1.0 |
| 5.3889988504485355 | 95.041 | -8.488 | 1.0 |
| 5.161161642533481 | 223.302 | -6.847 | 0.0 |
| 5.1619104721997 | 91.369 | -15.892 | 1.0 |
| 5.663321664900875 | 155.036 | -13.373 | 0.0 |
| 5.867111759061063 | 131.977 | -9.179 | 0.0 |
| 5.839595645377269 | 131.265 | -6.335 | 0.0 |
| 5.609491062398614 | 87.319 | -7.471 | 1.0 |
| 5.274624391653377 | 140.096 | -7.449 | 0.0 |
| 4.771303246638827 | 63.71 | -10.31 | 1.0 |
| 6.026909390629277 | 126.015 | -9.087 | 1.0 |
| 5.460239895734497 | 85.519 | -8.172 | 1.0 |
| 5.032305979406649 | 140.137 | -2.817 | 0.0 |
| 3.8527917624084433 | 111.193 | -20.757 | 1.0 |
| 5.550873690024442 | 100.014 | -8.463 | 1.0 |
| 6.128702349999979 | 126.998 | -9.481 | 1.0 |
| 5.850858502328606 | 96.111 | -14.782 | 1.0 |
| 5.883323391273292 | 97.304 | -6.351 | 1.0 |
| 5.35872276433664 | 86.691 | -4.353 | 1.0 |
| 5.734952664000064 | 114.219 | -3.163 | 1.0 |
| 5.318837824377515 | 116.906 | -17.986 | 1.0 |
| 5.497527627913457 | 89.112 | -2.579 | 1.0 |
| 5.902850659511112 | 130.005 | -5.964 | 1.0 |
| 5.219077332479166 | 98.044 | -8.327 | 1.0 |
| 4.852026748288437 | 56.4 | -26.583 | 1.0 |
| 5.387446848768176 | 153.759 | -7.641 | 0.0 |
| 5.019097058838546 | 93.415 | -7.098 | 1.0 |
| 5.630135755083509 | 174.595 | -7.042 | 0.0 |
| 5.681293243670071 | 160.029 | -7.049 | 0.0 |
| 4.690467127506071 | 109.941 | -12.103 | 1.0 |
| 4.886921078717307 | 156.884 | -8.85 | 0.0 |
| 3.7839566522346817 | 78.948 | -11.663 | 1.0 |
| 5.6159765639264005 | 92.998 | -5.958 | 1.0 |
| 5.723065947607238 | 126.023 | -13.288 | 1.0 |
| 5.11909033676264 | 91.969 | -10.539 | 1.0 |
| 5.639000688376509 | 150.027 | -9.959 | 0.0 |
| 5.283014929190587 | 98.844 | -12.454 | 1.0 |
| 5.334832065977185 | 237.604 | -20.672 | 0.0 |
| 5.6899827955592155 | 122.123 | -7.269 | 1.0 |
| 5.214826784280303 | 123.281 | -5.303 | 1.0 |
| 5.620245418853304 | 104.984 | -8.49 | 1.0 |
| 5.48003820127795 | 154.754 | -7.717 | 0.0 |
| 6.036257303707656 | 132.001 | -9.288 | 0.0 |
| 4.952703169254423 | 126.134 | -9.039 | 1.0 |
| 4.84032586198911 | 85.65 | -12.166 | 1.0 |
| 5.307125163770039 | 107.997 | -7.99 | 1.0 |
| 5.499558971737484 | 131.963 | -23.782 | 0.0 |
| 5.793629272482639 | 102.861 | -15.64 | 1.0 |
| 5.448057577397297 | 106.308 | -4.182 | 1.0 |
| 5.10935559222606 | 108.97 | -7.745 | 1.0 |
| 5.250118909785244 | 131.007 | -12.812 | 0.0 |
| 5.421045753770749 | 187.967 | -5.414 | 0.0 |
| 5.464673526949484 | 88.954 | -19.225 | 1.0 |
| 5.43333140326241 | 91.372 | -21.655 | 1.0 |
| 6.207548860529364 | 86.7 | -13.522 | 1.0 |
| 5.925707203507987 | 126.01 | -7.652 | 1.0 |
| 5.83418384470021 | 125.9 | -10.454 | 1.0 |
| 5.379530281569491 | 166.991 | -10.068 | 0.0 |
| 5.252856399447034 | 132.012 | -7.557 | 0.0 |
| 5.586650576963475 | 92.205 | -12.568 | 1.0 |
| 5.274891818392382 | 156.306 | -11.205 | 0.0 |
| 4.7561445982425585 | 181.832 | -5.36 | 0.0 |
| 6.046013502325825 | 91.214 | -8.872 | 1.0 |
| 5.656954494144676 | 133.205 | -8.568 | 0.0 |
| 5.469307759160471 | 119.99 | -13.474 | 1.0 |
| 5.492807583960489 | 112.015 | -7.135 | 1.0 |
| 5.51881068910637 | 142.751 | -15.649 | 0.0 |
| 4.74006814443605 | 154.067 | -3.63 | 0.0 |
| 5.59425741040241 | 135.616 | -10.088 | 0.0 |
| 5.099525150083379 | 111.869 | -6.919 | 1.0 |
| 5.452993065671398 | 150.525 | -11.524 | 0.0 |
| 5.441174600518283 | 91.374 | -6.715 | 1.0 |
| 5.532751854652075 | 162.048 | -5.726 | 0.0 |
| 5.079569452748531 | 119.939 | -10.924 | 1.0 |
| 5.154849628686833 | 107.51 | -10.176 | 1.0 |
| 5.610543052685526 | 80.025 | -10.408 | 1.0 |
| 5.561470781093142 | 155.746 | -8.475 | 0.0 |
| 5.72885716058872 | 131.971 | -7.201 | 0.0 |
| 5.196630096256507 | 112.52 | -22.935 | 1.0 |
| 6.677072604765031 | 86.596 | -9.254 | 1.0 |
| 5.276628504428926 | 67.183 | -17.95 | 1.0 |
| 5.774423369628363 | 91.166 | -16.059 | 1.0 |
| 5.122366215562913 | 132.742 | -6.337 | 0.0 |
| 4.737096290827358 | 84.435 | -22.572 | 1.0 |
| 5.400975032797074 | 129.749 | -12.142 | 1.0 |
| 5.363628963785261 | 88.222 | -6.461 | 1.0 |
| 5.523722678152987 | 104.592 | -5.825 | 1.0 |
| 5.840811177887705 | 128.373 | -8.574 | 1.0 |
| 5.607958929769235 | 115.338 | -5.719 | 1.0 |
| 5.325088257046393 | 101.662 | -10.225 | 1.0 |
| 6.105142010870422 | 119.992 | -10.292 | 1.0 |
| 5.175588953897928 | 97.619 | -18.724 | 1.0 |
| 5.367170955002224 | 119.032 | -4.0 | 1.0 |
| 5.272884218579013 | 123.695 | -7.73 | 1.0 |
| 5.090079730533548 | 130.771 | -10.753 | 1.0 |
| 4.90527744510154 | 106.427 | -17.549 | 1.0 |
| 5.291204127341661 | 104.378 | -12.13 | 1.0 |
| 4.726046477037809 | 117.436 | -26.354 | 1.0 |
| 4.914522552325214 | 156.8 | -5.616 | 0.0 |
| 3.645320906144505 | 190.071 | -8.348 | 0.0 |
| 6.217912752034596 | 127.986 | -15.299 | 1.0 |
| 5.49699239016298 | 152.552 | -9.21 | 0.0 |
| 4.91854054591316 | 171.014 | -4.493 | 0.0 |
| 5.748533253386812 | 125.986 | -9.382 | 1.0 |
| 5.686799476467082 | 105.387 | -13.275 | 1.0 |
| 5.7666855967574575 | 60.453 | -8.009 | 1.0 |
| 5.378686990009553 | 69.781 | -10.723 | 1.0 |
| 5.305311003955202 | 100.036 | -4.15 | 1.0 |
| 5.504991152805347 | 91.92 | -4.939 | 1.0 |
| 5.271409371809784 | 202.926 | -12.805 | 0.0 |
| 5.584003924284049 | 181.534 | -15.531 | 0.0 |
| 6.215096858959298 | 130.013 | -7.987 | 1.0 |
| 5.487092346805161 | 104.257 | -9.098 | 1.0 |
| 5.754096429647435 | 238.327 | -7.028 | 0.0 |
| 5.236179002727057 | 112.183 | -14.136 | 1.0 |
| 5.452545386241633 | 89.064 | -9.939 | 1.0 |
| 4.571933723436425 | 186.769 | -11.649 | 0.0 |
| 5.74878302284854 | 126.157 | -10.84 | 1.0 |
| 4.462453307085061 | 134.395 | -4.727 | 0.0 |
| 5.229765094411506 | 143.875 | -12.669 | 0.0 |
| 5.516293107703791 | 111.874 | -10.131 | 1.0 |
| 4.986986110278029 | 92.72 | -6.982 | 1.0 |
| 5.134594536181753 | 97.94 | -14.83 | 1.0 |
| 5.94764368547729 | 120.983 | -9.233 | 1.0 |
| 5.589973519371366 | 139.881 | -11.774 | 0.0 |
| 5.832577543586846 | 108.428 | -8.926 | 1.0 |
| 5.57404080623269 | 129.139 | -4.393 | 1.0 |
| 5.737734073968367 | 138.816 | -7.556 | 0.0 |
| 5.228365338789169 | 109.997 | -10.253 | 1.0 |
| 5.478948537110532 | 84.271 | -14.741 | 1.0 |
| 5.097132205437792 | 94.075 | -3.565 | 1.0 |
| 5.7915577015049 | 111.001 | -8.265 | 1.0 |
| 4.758836405626318 | 156.703 | -9.588 | 0.0 |
| 5.268318568522543 | 70.923 | -11.502 | 1.0 |
| 5.101912321360921 | 144.581 | -10.228 | 0.0 |
| 5.607287873701992 | 94.022 | -11.182 | 1.0 |
| 5.464231047351683 | 80.56 | -5.019 | 1.0 |
| 5.82628048105143 | 144.995 | -7.894 | 0.0 |
| 5.11862145971635 | 95.003 | -6.393 | 1.0 |
| 5.226542700885508 | 144.35 | -5.396 | 0.0 |
| 5.764803190566032 | 112.0 | -9.303 | 1.0 |
| 4.7352630988107265 | 114.697 | -4.575 | 1.0 |
| 5.316018719084458 | 141.976 | -21.61 | 0.0 |
| 5.733516795474041 | 183.021 | -11.119 | 0.0 |
| 5.305829678341554 | 86.122 | -4.442 | 1.0 |
| 5.934733177365839 | 124.97 | -18.222 | 1.0 |
| 5.253812766217677 | 96.969 | -3.996 | 1.0 |
| 5.411294114932506 | 89.874 | -6.8 | 1.0 |
| 4.551471791137433 | 80.11 | -12.882 | 1.0 |
| 5.24654895931225 | 126.857 | -11.178 | 1.0 |
| 5.52538935145276 | 88.999 | -7.108 | 1.0 |
| 5.233395411909235 | 174.002 | -4.727 | 0.0 |
| 5.062519840326649 | 172.201 | -4.75 | 0.0 |
| 5.47709335356482 | 130.832 | -19.444 | 1.0 |
| 6.546896021572621 | 175.921 | -14.61 | 0.0 |
| 5.875580463550244 | 125.238 | -8.638 | 1.0 |
| 5.547621681459602 | 182.05 | -8.294 | 0.0 |
| 5.600552539550706 | 165.987 | -5.869 | 0.0 |
| 5.330541303481353 | 100.578 | -13.527 | 1.0 |
| 5.521426437267798 | 98.619 | -8.239 | 1.0 |
| 5.0512127147107515 | 100.524 | -13.862 | 1.0 |
The new clustering model makes much more sense. Songs with high tempo and loudness are put in one cluster and song duration does not affect song categories.
To really understand how the points in 3D behave you need to see them in 3D interactively and understand the limits of its three 2D projections. For this let us spend some time and play in sageMath Worksheet in CoCalc (it is free for light-weight use and perhaps worth the 7 USD a month if you need more serious computing in mathematics, statistics, etc. in multiple languages!).
Let us take a look at this sageMath Worksheet published here:
- https://cocalc.com/share/ee9392a2-c83b-4eed-9468-767bb90fd12a/3DEuclideanSpace_1MSongsKMeansClustering.sagews?viewer=share
- and the accompanying datasets (downloaded from the
displays in this notebook and uploaded to CoCalc as CSV files):- https://cocalc.com/projects/ee9392a2-c83b-4eed-9468-767bb90fd12a/files/KMeansClusters10003DFeaturesloudness-tempologDurationOf1MSongsKMeansfor015sds2-2.csv
- https://cocalc.com/projects/ee9392a2-c83b-4eed-9468-767bb90fd12a/files/KMeansClusters10003DFeaturesloudness-tempoDurationOf1MSongsKMeansfor015sds2-2.csv
The point of the above little example is that you need to be able to tell a sensible story with your data science process and not just blindly apply a heuristic, but highly scalable, algorithm which depends on the notion of nearest neighborhoods defined by the metric (Euclidean distances in 3-dimensional real-valued spaces in this example) induced by the features you have engineered or have the power to re/re/...-engineer to increase the meaningfullness of the problem at hand.
Determining Optimal K
There are methods to find the optimal number of clusters. This partly depends on the purpose of the unsupervised learning task.
See here for some metrics in scala for the Irish dataset.
Supervised Clustering with Decision Trees
Visual Introduction to decision trees and application to hand-written digit recognition
SOURCE: This is just a couple of decorations on a notebook published in databricks community edition in 2016.
Decision Trees for handwritten digit recognition
This notebook demonstrates learning a Decision Tree using Spark's distributed implementation. It gives the reader a better understanding of some critical hyperparameters for the tree learning algorithm, using examples to demonstrate how tuning the hyperparameters can improve accuracy.
Background: To learn more about Decision Trees, check out the resources at the end of this notebook. The visual description of ML and Decision Trees provides nice intuition helpful to understand this notebook, and Wikipedia gives lots of details.
Data: We use the classic MNIST handwritten digit recognition dataset. It is from LeCun et al. (1998) and may be found under "mnist" at the LibSVM dataset page.
Goal: Our goal for our data is to learn how to recognize digits (0 - 9) from images of handwriting. However, we will focus on understanding trees, not on this particular learning problem.
Takeaways: Decision Trees take several hyperparameters which can affect the accuracy of the learned model. There is no one "best" setting for these for all datasets. To get the optimal accuracy, we need to tune these hyperparameters based on our data.
Let's Build Intuition for Learning with Decision Trees
- Right-click and open the following link in a new Tab:
- The visual description of ML and Decision Trees which was nominated for a NSF Vizzie award.
Load MNIST training and test datasets
Our datasets are vectors of pixels representing images of handwritten digits. For example:

These datasets are stored in the popular LibSVM dataset format. We will load them using MLlib's LibSVM dataset reader utility.
//-----------------------------------------------------------------------------------------------------------------
// using RDD-based MLlib - ok for Spark 1.x
// MLUtils.loadLibSVMFile returns an RDD.
//import org.apache.spark.mllib.util.MLUtils
//val trainingRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
//val testRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// We convert the RDDs to DataFrames to use with ML Pipelines.
//val training = trainingRDD.toDF()
//val test = testRDD.toDF()
// Note: In Spark 1.6 and later versions, Spark SQL has a LibSVM data source. The above lines can be simplified to:
//// val training = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-train.txt")
//// val test = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-test.txt")
//-----------------------------------------------------------------------------------------------------------------
val training = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/datasets/sds/mnist-digits/data-001/mnist-digits-train.txt")
val test = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/datasets/sds/mnist-digits/data-001/mnist-digits-test.txt")
// Cache data for multiple uses.
training.cache()
test.cache()
println(s"We have ${training.count} training images and ${test.count} test images.")
We have 60000 training images and 10000 test images.
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
test: org.apache.spark.sql.DataFrame = [label: double, features: vector]
Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).
training.printSchema()
root
|-- label: double (nullable = true)
|-- features: vector (nullable = true)
training.show(3) // replace 'true' by 'false' to see the whole row hidden by '...'
+-----+--------------------+
|label| features|
+-----+--------------------+
| 5.0|(780,[152,153,154...|
| 0.0|(780,[127,128,129...|
| 4.0|(780,[160,161,162...|
+-----+--------------------+
only showing top 3 rows
display(training) // this is databricks-specific for interactive visual convenience
The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) or training.show(2,false) above, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):
type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]
Here,
type: 0says we have a sparse vector that only represents non-zero entries (as opposed to a dense vector where every entry is represented).size: 780says the vector has 780 indices in total- these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
indices: [152,153,155,...,682,683]are the indices from the[0,1,...,779]possible indices with non-zero values- a value is an integer encoding the gray-level at the pixel index
values: [3, 18, 18,18,126,...,132,16]are the actual gray level values, for example:- at pixed index
152the gray-level value is3, - at index
153the gray-level value is18, - ..., and finally at
- at index
683the gray-level value is18
- at pixed index
Train a Decision Tree
We begin by training a decision tree using the default settings. Before training, we want to tell the algorithm that the labels are categories 0-9, rather than continuous values. We use the StringIndexer class to do this. We tie this feature preprocessing together with the tree algorithm using a Pipeline. ML Pipelines are tools Spark provides for piecing together Machine Learning algorithms into workflows. To learn more about Pipelines, check out other ML example notebooks in Databricks and the ML Pipelines user guide. Also See mllib-decision-tree.html#basic-algorithm.
// Import the ML algorithms we will use.
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
// StringIndexer: Read input column "label" (digits) and annotate them as categorical values.
val indexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel")
// DecisionTreeClassifier: Learn to predict column "indexedLabel" using the "features" column.
val dtc = new DecisionTreeClassifier().setLabelCol("indexedLabel")
// Chain indexer + dtc together into a single ML Pipeline.
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
indexer: org.apache.spark.ml.feature.StringIndexer = strIdx_cdb9fb742f94
dtc: org.apache.spark.ml.classification.DecisionTreeClassifier = dtc_9345fcaec0f5
pipeline: org.apache.spark.ml.Pipeline = pipeline_a426bd538000
Now, let's fit a model to our data.
val model = pipeline.fit(training)
model: org.apache.spark.ml.PipelineModel = pipeline_a426bd538000
We can inspect the learned tree by displaying it using Databricks ML visualization. (Visualization is available for several but not all models.)
// The tree is the last stage of the Pipeline. Display it!
val tree = model.stages.last.asInstanceOf[DecisionTreeClassificationModel]
display(tree)
| treeNode |
|---|
| {"index":31,"featureType":"continuous","prediction":null,"threshold":133.5,"categories":null,"feature":350,"overflow":false} |
| {"index":15,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":568,"overflow":false} |
| {"index":7,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":430,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":2.5,"categories":null,"feature":405,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":12.5,"categories":null,"feature":485,"overflow":false} |
| {"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":14.5,"categories":null,"feature":516,"overflow":false} |
| {"index":4,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":34.5,"categories":null,"feature":211,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":98,"overflow":false} |
| {"index":8,"featureType":null,"prediction":8.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":156,"overflow":false} |
| {"index":12,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":23,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":435,"overflow":false} |
| {"index":19,"featureType":"continuous","prediction":null,"threshold":10.5,"categories":null,"feature":489,"overflow":false} |
| {"index":17,"featureType":"continuous","prediction":null,"threshold":2.5,"categories":null,"feature":380,"overflow":false} |
| {"index":16,"featureType":null,"prediction":5.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":18,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":21,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":320,"overflow":false} |
| {"index":20,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":22,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":27,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":346,"overflow":false} |
| {"index":25,"featureType":"continuous","prediction":null,"threshold":97.5,"categories":null,"feature":348,"overflow":false} |
| {"index":24,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":26,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":29,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":655,"overflow":false} |
| {"index":28,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":30,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":47,"featureType":"continuous","prediction":null,"threshold":36.5,"categories":null,"feature":489,"overflow":false} |
| {"index":39,"featureType":"continuous","prediction":null,"threshold":26.5,"categories":null,"feature":290,"overflow":false} |
| {"index":35,"featureType":"continuous","prediction":null,"threshold":100.5,"categories":null,"feature":486,"overflow":false} |
| {"index":33,"featureType":"continuous","prediction":null,"threshold":129.5,"categories":null,"feature":490,"overflow":false} |
| {"index":32,"featureType":null,"prediction":2.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":34,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":37,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":656,"overflow":false} |
| {"index":36,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":38,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":43,"featureType":"continuous","prediction":null,"threshold":5.5,"categories":null,"feature":297,"overflow":false} |
| {"index":41,"featureType":"continuous","prediction":null,"threshold":169.5,"categories":null,"feature":486,"overflow":false} |
| {"index":40,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":42,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":45,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":598,"overflow":false} |
| {"index":44,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":46,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":55,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":521,"overflow":false} |
| {"index":51,"featureType":"continuous","prediction":null,"threshold":7.5,"categories":null,"feature":347,"overflow":false} |
| {"index":49,"featureType":"continuous","prediction":null,"threshold":4.5,"categories":null,"feature":206,"overflow":false} |
| {"index":48,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":50,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":53,"featureType":"continuous","prediction":null,"threshold":16.5,"categories":null,"feature":514,"overflow":false} |
| {"index":52,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":54,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":59,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":658,"overflow":false} |
| {"index":57,"featureType":"continuous","prediction":null,"threshold":8.5,"categories":null,"feature":555,"overflow":false} |
| {"index":56,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":58,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":61,"featureType":"continuous","prediction":null,"threshold":12.5,"categories":null,"feature":543,"overflow":false} |
| {"index":60,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":62,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Above, we can see how the tree makes predictions. When classifying a new example, the tree starts at the "root" node (at the top). Each tree node tests a pixel value and goes either left or right. At the bottom "leaf" nodes, the tree predicts a digit as the image's label.
Hyperparameter Tuning
Run the next cell and come back into hyper-parameter tuning for a couple minutes.
Exploring "maxDepth": training trees of different sizes
In this section, we test tuning a single hyperparameter maxDepth, which determines how deep (and large) the tree can be. We will train trees at varying depths and see how it affects the accuracy on our held-out test set.
Note: The next cell can take about 1 minute to run since it is training several trees which get deeper and deeper.
val variedMaxDepthModels = (0 until 8).map { maxDepth =>
// For this setting of maxDepth, learn a decision tree.
dtc.setMaxDepth(maxDepth)
// Create a Pipeline with our feature processing stage (indexer) plus the tree algorithm
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
// Run the ML Pipeline to learn a tree.
pipeline.fit(training)
}
variedMaxDepthModels: scala.collection.immutable.IndexedSeq[org.apache.spark.ml.PipelineModel] = Vector(pipeline_e96074b3bded, pipeline_12646faf8260, pipeline_d5f639d6a180, pipeline_4034c9138161, pipeline_83215a1ed684, pipeline_8737125f5dcc, pipeline_02a6caa4d542, pipeline_88827be266ae)
We will use the default metric to evaluate the performance of our classifier:
// Define an evaluation metric. In this case, we will use "accuracy".
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setMetricName("f1") // default MetricName
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_0ee0afcb7621, metricName=f1, metricLabel=0.0, beta=1.0, eps=1.0E-15
// For each maxDepth setting, make predictions on the test data, and compute the classifier's f1 performance metric.
val f1MetricPerformanceMeasures = (0 until 8).map { maxDepth =>
val model = variedMaxDepthModels(maxDepth)
// Calling transform() on the test set runs the fitted pipeline.
// The learned model makes predictions on each test example.
val predictions = model.transform(test)
// Calling evaluate() on the predictions DataFrame computes our performance metric.
(maxDepth, evaluator.evaluate(predictions))
}.toDF("maxDepth", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxDepth: int, f1: double]
We can display our accuracy results and see immediately that deeper, larger trees are more powerful classifiers, achieving higher accuracies.
Note: When you run f1MetricPerformanceMeasures.show(), you will get a table with f1 score getting better (i.e., approaching 1) with depth.
f1MetricPerformanceMeasures.show()
+--------+-------------------+
|maxDepth| f1|
+--------+-------------------+
| 0| 0.023138302649304|
| 1|0.07692344325297736|
| 2|0.21454218035530098|
| 3|0.43262516590643385|
| 4| 0.5918539983626627|
| 5| 0.6806954435278861|
| 6| 0.7478698562916142|
| 7| 0.7876393954574569|
+--------+-------------------+
Even though deeper trees are more powerful, they are not always better (recall from the SF/NYC city classification from house features at The visual description of ML and Decision Trees). If we kept increasing the depth on a rich enough dataset, training would take longer and longer. We also might risk overfitting (fitting the training data so well that our predictions get worse on test data); it is important to tune parameters based on held-out data to prevent overfitting. This will ensure that the fitted model generalizes well to yet unseen data, i.e. minimizes generalization error in a mathematical statistical sense.
Exploring "maxBins": discretization for efficient distributed computing
This section explores a more expert-level setting maxBins. For efficient distributed training of Decision Trees, Spark and most other libraries discretize (or "bin") continuous features (such as pixel values) into a finite number of values. This is an important step for the distributed implementation, but it introduces a tradeoff: Larger maxBins mean your data will be more accurately represented, but it will also mean more communication (and slower training).
The default value of maxBins generally works, but it is interesting to explore on our handwritten digit dataset. Remember our digit image from above:

It is grayscale. But if we set maxBins = 2, then we are effectively making it a black-and-white image, not grayscale. Will that affect the accuracy of our model? Let's see!
Note: The next cell can take about 35 seconds to run since it trains several trees. Read the details on maxBins at mllib-decision-tree.html#split-candidates.
dtc.setMaxDepth(6) // Set maxDepth to a reasonable value.
// now try the maxBins "hyper-parameter" which actually acts as a "coarsener"
// mathematical researchers should note that it is a sub-algebra of the finite
// algebra of observable pixel images at the finest resolution available to us
// giving a compression of the image to fewer coarsely represented pixels
val f1MetricPerformanceMeasures = Seq(2, 4, 8, 16, 32).map { case maxBins =>
// For this value of maxBins, learn a tree.
dtc.setMaxBins(maxBins)
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
val model = pipeline.fit(training)
// Make predictions on test data, and compute accuracy.
val predictions = model.transform(test)
(maxBins, evaluator.evaluate(predictions))
}.toDF("maxBins", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxBins: int, f1: double]
f1MetricPerformanceMeasures.show()
+-------+------------------+
|maxBins| f1|
+-------+------------------+
| 2|0.7429289482693366|
| 4|0.7390759023032782|
| 8|0.7443669963407805|
| 16|0.7426765688669141|
| 32|0.7478698562916142|
+-------+------------------+
We can see that extreme discretization (black and white) hurts performance as measured by F1-error, but only a bit. Using more bins increases the accuracy (but also makes learning more costly).
What's next?
- Explore: Try out tuning other parameters of trees---or even ensembles like Random Forests or Gradient-Boosted Trees.
- Automated tuning: This type of tuning does not have to be done by hand. (We did it by hand here to show the effects of tuning in detail.) MLlib provides automated tuning functionality via
CrossValidator. Check out the other Databricks ML Pipeline guides or the Spark ML user guide for details.
Resources
If you are interested in learning more on these topics, these resources can get you started:
- Excellent visual description of Machine Learning and Decision Trees
- This gives an intuitive visual explanation of ML, decision trees, overfitting, and more.
- Blog post on MLlib Random Forests and Gradient-Boosted Trees
- Random Forests and Gradient-Boosted Trees combine many trees into more powerful ensemble models. This is the original post describing MLlib's forest and GBT implementations.
- Wikipedia
Linear Algebra Review
This is a breeze'y scalarific break-down of:
- Home Work: read this! https://github.com/scalanlp/breeze/wiki/Quickstart
Using the above resources we'll provide a review of basic linear algebra concepts that will recur throughout the course. These concepts include:
- Matrices
- Vectors
- Arithmetic operations with vectors and matrices
We will see the accompanying Scala computations in the local or non-distributed setting.
Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications, eigen systems with real and comples Eigen values NOW from the following interactive visual-cognitive aid at:
- http://setosa.io/ev/eigenvectors-and-eigenvalues/ just focus on geometric interpretation of vectors and matrices in Cartesian coordinates.
Breeze is a linear algebra package in Scala. First, let us import it as follows:
import breeze.linalg._
import breeze.linalg._
1. Matrix: creation and element-access
A matrix is a two-dimensional array.
Let us denote matrices via bold uppercase letters as follows:
For instance, the matrix below is denoted with \(\mathbf{A}\), a capital bold A.
\[ \mathbf{A} = \begin{pmatrix} a_{11} & a_{12} & a_{13} \ a_{21} & a_{22} & a_{23} \ a_{31} & a_{32} & a_{33} \end{pmatrix} \]
We usually put commas between the row and column indexing sub-scripts, to make the possibly multi-digit indices distinguishable as follows:
\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \ a_{3,1} & a_{3,2} & a_{3,3} \end{pmatrix} \]
- \(\mathbf{A}_{i,j}\) denotes the entry in \(i\)-th row and \(j\)-th column of the matrix \(\mathbf{A}\).
- So for instance,
- the first entry, the top left entry, is denoted by \(\mathbf{A}_{1,1}\).
- And the entry in the third row and second column is denoted by \(\mathbf{A}_{3,2}\).
- We say that a matrix with n rows and m columns is an \(n\) by \(m\) matrix and written as \(n \times m\)
- The matrix \(\mathbf{A}\) shown above is a generic \(3 \times 3\) (pronounced 3-by-3) matrix.
- And the matrix in Ameet's example in the video above, having 4 rows and 3 columns, is a 4 by 3 matrix.
- If a matrix \(\mathbf{A}\) is \(n \times m\), we write:
- \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
- where, \(\mathbb{R}\) here denotes the set of all real numbers in the line given by the open interval: \((-\infty,+\infty)\).
- \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
Let us created a matrix A as a val (that is immutable) in scala. The matrix we want to create is mathematically notated as follows:
\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \end{pmatrix}
\begin{pmatrix} 1 & 2 & 3 \ 4 & 5 & 6 \end{pmatrix} \]
val A = DenseMatrix((1, 2, 3), (4, 5, 6)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
A.size
res0: Int = 6
A.rows // number of rows
res1: Int = 2
A.size / A.rows // num of columns
res2: Int = 3
A.cols // also say
res3: Int = 3
Now, let's access the element \(a_{1,1}\), i.e., the element from the first row and first column of \(\mathbf{A}\), which in our val A matrix is the integer of type Int equalling 1.
A(0, 0) // Remember elements are indexed by zero in scala
res4: Int = 1
Gotcha: indices in breeze matrices start at 0 as in numpy of python and not at 1 as in MATLAB!
Of course if you assign the same dense matrix to a mutable var B then its entries can be modified as follows:
var B = DenseMatrix((1, 2, 3), (4, 5, 6))
B: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
B(0,0)=999; B(1,1)=969; B(0,2)=666
B
res5: breeze.linalg.DenseMatrix[Int] =
999 2 666
4 969 6
-
A vector is a matrix with many rows and one column.
-
We'll denote a vector by bold lowercase letters: \[\mathbf{a} = \begin{pmatrix} 3.3 \ 1.0 \ 6.3 \ 3.6 \end{pmatrix}\]
So, the vector above is denoted by \(\mathbf{a}\), the lowercase, bold a.
-
\(a_i\) denotes the i-th entry of a vector. So for instance:
- \(a_2\) denotes the second entry of the vector and it is 1.0 for our vector.
-
If a vector is m-dimensional, then we say that \(\mathbf{a}\) is in \(\mathbb{R}^m\) and write \(\mathbf{a} \in \ \mathbb{R}^m\).
- So our \(\mathbf{a} \in \ \mathbb{R}^4\).
val a = DenseVector(3.3, 1.0, 6.3, 3.6) // these are row vectors
a: breeze.linalg.DenseVector[Double] = DenseVector(3.3, 1.0, 6.3, 3.6)
a.size // a is a column vector of size 4
res6: Int = 4
a(1) // the second element of a is indexed by 1 as the first element is indexed by 0
res7: Double = 1.0
val a = DenseVector[Double](5, 4, -1) // this makes a vector of Doubles from input Int
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val a = DenseVector(5.0, 4.0, -1.0) // this makes a vector of Doubles from type inference . NOTE "5.0" is needed not just "5."
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val x = DenseVector.zeros[Double](5) // this will output x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
val A = DenseMatrix((1, 4), (6, 1), (3, 5)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
A.t // transpose of A
res8: breeze.linalg.DenseMatrix[Int] =
1 6 3
4 1 5
val a = DenseVector(3.0, 4.0, 1.0)
a: breeze.linalg.DenseVector[Double] = DenseVector(3.0, 4.0, 1.0)
a.t
res9: breeze.linalg.Transpose[breeze.linalg.DenseVector[Double]] = Transpose(DenseVector(3.0, 4.0, 1.0))
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
val B = -A
B: breeze.linalg.DenseMatrix[Int] =
-1 -4
-6 -1
-3 -5
A + B // should be A-A=0
res10: breeze.linalg.DenseMatrix[Int] =
0 0
0 0
0 0
A - B // should be A+A=2A
res11: breeze.linalg.DenseMatrix[Int] =
2 8
12 2
6 10
B - A // should be -A-A=-2A
res12: breeze.linalg.DenseMatrix[Int] =
-2 -8
-12 -2
-6 -10
Operators
All Tensors support a set of operators, similar to those used in Matlab or Numpy.
For HOMEWORK see: Workspace -> scalable-data-science -> xtraResources -> LinearAlgebra -> LAlgCheatSheet for a list of most of the operators and various operations.
Some of the basic ones are reproduced here, to give you an idea.
| Operation | Breeze | Matlab | Numpy |
|---|---|---|---|
| Elementwise addition | a + b | a + b | a + b |
| Elementwise multiplication | a :* b | a .* b | a * b |
| Elementwise comparison | a :< b | a < b (gives matrix of 1/0 instead of true/false) | a < b |
| Inplace addition | a :+= 1.0 | a += 1 | a += 1 |
| Inplace elementwise multiplication | a :*= 2.0 | a *= 2 | a *= 2 |
| Vector dot product | a dot b,a.t * b† | dot(a,b) | dot(a,b) |
| Elementwise sum | sum(a) | sum(sum(a)) | a.sum() |
| Elementwise max | a.max | max(a) | a.max() |
| Elementwise argmax | argmax(a) | argmax(a) | a.argmax() |
| Ceiling | ceil(a) | ceil(a) | ceil(a) |
| Floor | floor(a) | floor(a) | floor(a) |
Pop Quiz:
- what is a natural geometric interpretation of scalar multiplication of a vector or a matrix and what about vector matrix multiplication?
Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications from the first interactive visual-cognitive aid at:
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
5 * A
res13: breeze.linalg.DenseMatrix[Int] =
5 20
30 5
15 25
A * 5
res14: breeze.linalg.DenseMatrix[Int] =
5 20
30 5
15 25
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
val B = DenseMatrix((3, 1), (2, 2), (1, 3))
B: breeze.linalg.DenseMatrix[Int] =
3 1
2 2
1 3
A *:* B // element-wise multiplication
res15: breeze.linalg.DenseMatrix[Int] =
3 4
12 2
3 15
val A = DenseMatrix((1, 4), (3, 1))
A: breeze.linalg.DenseMatrix[Int] =
1 4
3 1
val a = DenseVector(1, -1) // is a column vector
a: breeze.linalg.DenseVector[Int] = DenseVector(1, -1)
a.size // a is a column vector of size 2
res16: Int = 2
A * a
res17: breeze.linalg.DenseVector[Int] = DenseVector(-3, 2)
val A = DenseMatrix((1,2,3),(1,1,1))
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
1 1 1
val B = DenseMatrix((4, 1), (9, 2), (8, 9))
B: breeze.linalg.DenseMatrix[Int] =
4 1
9 2
8 9
A*B // 4+18+14
res18: breeze.linalg.DenseMatrix[Int] =
46 32
21 12
1*4 + 2*9 + 3*8 // checking first entry of A*B
res19: Int = 46
val A = DenseMatrix((1,2,3),(4,5,6))
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
DenseMatrix.eye[Int](3)
res20: breeze.linalg.DenseMatrix[Int] =
1 0 0
0 1 0
0 0 1
A * DenseMatrix.eye[Int](3)
res21: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
DenseMatrix.eye[Int](2) * A
res22: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
val D = DenseMatrix((2.0, 3.0), (4.0, 5.0))
val Dinv = inv(D)
D: breeze.linalg.DenseMatrix[Double] =
2.0 3.0
4.0 5.0
Dinv: breeze.linalg.DenseMatrix[Double] =
-2.5 1.5
2.0 -1.0
D * Dinv
res23: breeze.linalg.DenseMatrix[Double] =
1.0 0.0
0.0 1.0
Dinv * D
res24: breeze.linalg.DenseMatrix[Double] =
1.0 0.0
0.0 1.0
val b = DenseVector(4, 3)
norm(b)
b: breeze.linalg.DenseVector[Int] = DenseVector(4, 3)
res25: Double = 5.0
Math.sqrt(4*4 + 3*3) // check
res26: Double = 5.0
HOMEWORK: read this! https://github.com/scalanlp/breeze/wiki/Quickstart
It is here in markdown'd via wget and pandoc for your convenience.
Scala / nlp / breeze / Quickstart
David Hall edited this page on 24 Dec 2015
Breeze is modeled on Scala, and so if you're familiar with it, you'll be familiar with Breeze. First, import the linear algebra package:
scala> import breeze.linalg._
Let's create a vector:
scala> val x = DenseVector.zeros[Double](5)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
Here we make a column vector of zeros of type Double. And there are other ways we could create the vector - such as with a literal DenseVector(1,2,3) or with a call to fill or tabulate. The vector is "dense" because it is backed by an Array[Double], but could as well have created a SparseVector.zeros[Double](5), which would not allocate memory for zeros.
Unlike Scalala, all Vectors are column vectors. Row vectors are represented as Transpose[Vector[T]].
The vector object supports accessing and updating data elements by their index in 0 to x.length-1. Like Numpy, negative indices are supported, with the semantics that for an index i < 0 we operate on the i-th element from the end (x(i) == x(x.length + i)).
scala> x(0)
Double = 0.0
scala> x(1) = 2
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.0, 0.0)
Breeze also supports slicing. Note that slices using a Range are much, much faster than those with an arbitrary sequence.
scala> x(3 to 4) := .5
breeze.linalg.DenseVector[Double] = DenseVector(0.5, 0.5)
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.5, 0.5)
The slice operator constructs a read-through and write-through view of the given elements in the underlying vector. You set its values using the vectorized-set operator :=. You could as well have set it to a compatibly sized Vector.
scala> x(0 to 1) := DenseVector(.1,.2)
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.1, 0.2, 0.0, 0.5, 0.5)
Similarly, a DenseMatrix can be created with a constructor method call, and its elements can be accessed and updated.
scala> val m = DenseMatrix.zeros[Int](5,5)
m: breeze.linalg.DenseMatrix[Int] =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
The columns of m can be accessed as DenseVectors, and the rows as DenseMatrices.
scala> (m.rows, m.cols)
(Int, Int) = (5,5)
scala> m(::,1)
breeze.linalg.DenseVector[Int] = DenseVector(0, 0, 0, 0, 0)
scala> m(4,::) := DenseVector(1,2,3,4,5).t // transpose to match row shape
breeze.linalg.DenseMatrix[Int] = 1 2 3 4 5
scala> m
breeze.linalg.DenseMatrix[Int] =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
1 2 3 4 5
Assignments with incompatible cardinality or a larger numeric type won't compile.
scala> m := x
<console>:13: error: could not find implicit value for parameter op: breeze.linalg.operators.BinaryUpdateOp[breeze.linalg.DenseMatrix[Int],breeze.linalg.DenseVector[Double],breeze.linalg.operators.OpSet]
m := x
^
Assignments with incompatible size will throw an exception:
scala> m := DenseMatrix.zeros[Int](3,3)
java.lang.IllegalArgumentException: requirement failed: Matrices must have same number of row
Sub-matrices can be sliced and updated, and literal matrices can be specified using a simple tuple-based syntax. Unlike Scalala, only range slices are supported, and only the columns (or rows for a transposed matrix) can have a Range step size different from 1.
scala> m(0 to 1, 0 to 1) := DenseMatrix((3,1),(-1,-2))
breeze.linalg.DenseMatrix[Int] =
3 1
-1 -2
scala> m
breeze.linalg.DenseMatrix[Int] =
3 1 0 0 0
-1 -2 0 0 0
0 0 0 0 0
0 0 0 0 0
1 2 3 4 5
Broadcasting
Sometimes we want to apply an operation to every row or column of a matrix, as a unit. For instance, you might want to compute the mean of each row, or add a vector to every column. Adapting a matrix so that operations can be applied column-wise or row-wise is called broadcasting. Languages like R and numpy automatically and implicitly do broadcasting, meaning they won't stop you if you accidentally add a matrix and a vector. In Breeze, you have to signal your intent using the broadcasting operator *. The * is meant to evoke "foreach" visually. Here are some examples:
scala> import breeze.stats.mean
scala> val dm = DenseMatrix((1.0,2.0,3.0),
(4.0,5.0,6.0))
scala> val res = dm(::, *) + DenseVector(3.0, 4.0)
breeze.linalg.DenseMatrix[Double] =
4.0 5.0 6.0
8.0 9.0 10.0
scala> res(::, *) := DenseVector(3.0, 4.0)
scala> res
breeze.linalg.DenseMatrix[Double] =
3.0 3.0 3.0
4.0 4.0 4.0
scala> mean(dm(*, ::))
breeze.linalg.DenseVector[Double] = DenseVector(2.0, 5.0)
breeze.stats.distributions
Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.
scala> import breeze.stats.distributions._
scala> val poi = new Poisson(3.0);
poi: breeze.stats.distributions.Poisson = <function1>
scala> val s = poi.sample(5);
s: IndexedSeq[Int] = Vector(5, 4, 5, 7, 4)
scala> s map { poi.probabilityOf(_) }
IndexedSeq[Double] = Vector(0.10081881344492458, 0.16803135574154085, 0.10081881344492458, 0.02160403145248382, 0.16803135574154085)
scala> val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = breeze.stats.distributions.Rand$$anon$11@1b52e04
scala> breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.9960000000000067,2.9669509509509533,1000)
scala> (poi.mean,poi.variance)
(Double, Double) = (3.0,3.0)
NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.
scala> val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
scala> expo.rate
Double = 0.5
A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.
scala> expo.probability(0, log(2) * expo.rate)
Double = 0.5
scala> expo.probability(0.0, 1.5)
Double = 0.950212931632136
This means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well
scala> 1 - exp(-3.0)
Double = 0.950212931632136
scala> val samples = expo.sample(2).sorted;
samples: IndexedSeq[Double] = Vector(1.1891135726280517, 2.325607782657507)
scala> expo.probability(samples(0), samples(1));
Double = 0.08316481553047272
scala> breeze.stats.meanAndVariance(expo.samples.take(10000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.029351863973081,4.163267835527843,10000)
scala> (1 / expo.rate, 1 / (expo.rate * expo.rate))
(Double, Double) = (2.0,4.0)
breeze.optimize
TODO: document breeze.optimize.minimize, recommend that instead.
Breeze's optimization package includes several convex optimization routines and a simple linear program solver. Convex optimization routines typically take a DiffFunction[T], which is a Function1 extended to have a gradientAt method, which returns the gradient at a particular point. Most routines will require a breeze.linalg-enabled type: something like a Vector or a Counter.
Here's a simple DiffFunction: a parabola along each vector's coordinate.
scala> import breeze.optimize._
scala> val f = new DiffFunction[DenseVector[Double]] {
| def calculate(x: DenseVector[Double]) = {
| (norm((x - 3d) :^ 2d,1d),(x * 2d) - 6d);
| }
| }
f: java.lang.Object with breeze.optimize.DiffFunction[breeze.linalg.DenseVector[Double]] = $anon$1@617746b2
Note that this function takes its minimum when all values are 3. (It's just a parabola along each coordinate.)
scala> f.valueAt(DenseVector(3,3,3))
Double = 0.0
scala> f.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(0.0, -6.0, -4.0)
scala> f.calculate(DenseVector(0,0))
(Double, breeze.linalg.DenseVector[Double]) = (18.0,DenseVector(-6.0, -6.0))
You can also use approximate derivatives, if your function is easy enough to compute:
scala> def g(x: DenseVector[Double]) = (x - 3.0):^ 2.0 sum
scala> g(DenseVector(0.,0.,0.))
Double = 27.0
scala> val diffg = new ApproximateGradientFunction(g)
scala> diffg.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(1.000000082740371E-5, -5.999990000127297, -3.999990000025377)
Ok, now let's optimize f. The easiest routine to use is just LBFGS, which is a quasi-Newton method that works well for most problems.
scala> val lbfgs = new LBFGS[DenseVector[Double]](maxIter=100, m=3) // m is the memory. anywhere between 3 and 7 is fine. The larger m, the more memory is needed.
scala> val optimum = lbfgs.minimize(f,DenseVector(0,0,0))
optimum: breeze.linalg.DenseVector[Double] = DenseVector(2.9999999999999973, 2.9999999999999973, 2.9999999999999973)
scala> f(optimum)
Double = 2.129924444096732E-29
That's pretty close to 0! You can also use a configurable optimizer, using FirstOrderMinimizer.OptParams. It takes several parameters:
case class OptParams(batchSize:Int = 512,
regularization: Double = 1.0,
alpha: Double = 0.5,
maxIterations:Int = -1,
useL1: Boolean = false,
tolerance:Double = 1E-4,
useStochastic: Boolean= false) {
// ...
}
batchSize applies to BatchDiffFunctions, which support using small minibatches of a dataset. regularization integrates L2 or L1 (depending on useL1) regularization with constant lambda. alpha controls the initial stepsize for algorithms that need it. maxIterations is the maximum number of gradient steps to be taken (or -1 for until convergence). tolerance controls the sensitivity of the convergence check. Finally, useStochastic determines whether or not batch functions should be optimized using a stochastic gradient algorithm (using small batches), or using LBFGS (using the entire dataset).
OptParams can be controlled using breeze.config.Configuration, which we described earlier.
breeze.optimize.linear
We provide a DSL for solving linear programs, using Apache's Simplex Solver as the backend. This package isn't industrial strength yet by any means, but it's good for simple problems. The DSL is pretty simple:
import breeze.optimize.linear._
val lp = new LinearProgram()
import lp._
val x0 = Real()
val x1 = Real()
val x2 = Real()
val lpp = ( (x0 + x1 * 2 + x2 * 3 )
subjectTo ( x0 * -1 + x1 + x2 <= 20)
subjectTo ( x0 - x1 * 3 + x2 <= 30)
subjectTo ( x0 <= 40 )
)
val result = maximize( lpp)
assert( norm(result.result - DenseVector(40.0,17.5,42.5), 2) < 1E-4)
We also have specialized routines for bipartite matching (KuhnMunkres and CompetitiveLinking) and flow problems.
Where to go next?
After reading this quickstart, you can go to other wiki pages, especially Linear Algebra Cheat-Sheet and Data Structures.
Review the following for example:
if you have not experienced or forgotten big-O and big-Omega and big-Theta notation to study the computational efficiency of algorithms.
Let us visit an interactive visual cognitive tool for the basics ideas in linear regression:
Linear Regression by Hastie and Tibshirani
Chapter 3 of https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
The following video playlist is a very concise and thorough treatment of linear regression for those who have taken the 200-level linear algebra. Others can fully understand it with some effort and revisiting.
Ridge regression has a Bayesian interpretation where the weights have a zero-mean Gaussian prior. See 7.5 in Murphy's Machine Learning: A Probabilistic Perspective for details.
Please take notes in mark-down if you want.
For latex math within markdown you can do the following for in-line maths: \(\mathbf{A}_{i,j} \in \mathbb{R}^1\). And to write maths in display mode do the following:
\[\mathbf{A} \in \mathbb{R}^{m \times d} \]
You will need to write such notes for your final project presentation!
Gradient Descent
Refresher: what-is-gradient-descent
Getting our hands dirty with Darren Wilkinson's blog on regression
Let's follow the exposition in:
- https://darrenjw.wordpress.com/2017/02/08/a-quick-introduction-to-apache-spark-for-statisticians/
- https://darrenjw.wordpress.com/2017/06/21/scala-glm-regression-modelling-in-scala/
You need to scroll down the fitst link embedded in iframe below to get to the section on Analysis of quantitative data with Descriptive statistics and Linear regression sub-sections (you can skip the earlier part that shows you how to run everything locally in spark-shell - try this on your own later).
Let's do this live now...
import breeze.stats.distributions._
def x = Gaussian(1.0,2.0).sample(10000)
val xRdd = sc.parallelize(x)
import breeze.stats.distributions._
x: IndexedSeq[Double]
xRdd: org.apache.spark.rdd.RDD[Double] = ParallelCollectionRDD[0] at parallelize at command-2971213210277124:3
println(xRdd.mean)
println(xRdd.sampleVariance)
0.9961211853107911
4.025384520599017
val xStats = xRdd.stats
xStats.mean
xStats.sampleVariance
xStats.sum
xStats: org.apache.spark.util.StatCounter = (count: 10000, mean: 0.996121, stdev: 2.006236, max: 8.326542, min: -6.840659)
res1: Double = 9961.21185310791
val x2 = Gaussian(0.0,1.0).sample(10000) // 10,000 Gaussian samples with mean 0.0 and std dev 1.0
val xx = x zip x2 // creating tuples { (x,x2)_1, ... , (x,x2)_10000}
val lp = xx map {p => 2.0*p._1 + 1.0*p._2 + 1.5} // { lp_i := (2.0*x + 1.0 * x2 + 1.5)_i, i=1,...,10000 }
val eps = Gaussian(0.0,1.0).sample(10000) // standrad normal errors
val y = (lp zip eps) map (p => p._1 + p._2)
val yx = (y zip xx) map (p => (p._1, p._2._1, p._2._2))
x2: IndexedSeq[Double] = Vector(0.1510498720110515, -0.21709023201809416, -0.9420189342233366, -0.6050521762158899, 0.48771393128636853, 0.027986752952643124, 0.08206698908429294, -0.7810931162704136, -1.2057806605697985, -0.9176042669882863, 1.1959113751272468, 0.5771223111098949, -0.5030579509564024, 0.05792512611966754, 2.0492117764647917, -1.6192400586380957, -0.28568171141353893, 1.662660432138664, -0.3053683539449641, 0.3879563181609168, -0.7733054017369991, -0.39556455561853454, 1.7717311537878704, 0.33232937623812936, -0.1392031424321576, -0.5587152832826312, -1.7575839385785728, -0.6579581256900572, 2.037190332203657, 0.03462069676057432, -0.3795037160416964, 0.4622268686186779, -1.0897313199516543, -1.6880936202156007, -0.19374927120525648, -0.5509651353004006, -1.7588836379571422, -0.28930357427256465, 0.4020360571731675, -1.6301541413671794, 0.5958281089725639, 0.366330814768569, 0.4073858014158006, 0.16855406345881346, -0.947375008877488, -1.301502215739479, 0.1470275032863848, 1.0019450542117738, -0.5904174670089379, 0.5435130824204192, 0.3257024438513822, 0.7490447645980914, 0.7972538819971793, -3.008935968399453, -0.2458138862820675, 2.2893054863712985, -0.16582870751291443, -0.7663457059098656, -0.02129165082715293, -1.678393618311418, 0.4477301980923963, -1.3761630188037661, 0.8348043252317754, 2.593999513789406, 0.6054528239524932, -0.517937225538966, -0.8704495697165462, 0.7963689735065775, -0.22860472183526676, -0.8133035588543074, 1.1807570202429787, 0.09708549308780165, -0.1255029357381233, -0.23174042403046444, 1.9877912284224935, -1.6162508631493637, -0.1284467343953938, 0.715203575233169, 0.3144454735478882, -0.1848647516127121, -0.5496887866336722, -0.2545756305645813, 0.16750436775298258, -2.4673043543431854, 1.1418851052679557, 0.3748790073641352, -0.817404869860111, -0.8361798593188912, -1.5394267146879406, -0.41572364800501355, -0.1012436049109305, -1.1015184209065036, 1.8240944932710277, -0.028399443888511452, 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(2.7320211668089156,-2.3731627585033115), (2.812558906168454,0.43524872907522616), (-0.056923496414450714,0.6328073362083871), (-0.6014131312820246,-0.2381862432808314), (-3.5196269653783325,0.40492896291344244), (-1.1067214502928073,-1.0527840343286374), (-0.4736116098154268,1.0689442908821987), (2.6403094933932953,0.789255581937264), (1.5982824524769197,1.1724902795494772), (-0.09123150857004858,1.2893390188349818), (0.3777640975590455,0.8926054903089587), (1.5757168418364564,1.4246842661952515), (-0.24202485980852528,0.21910907895561132), (4.589933486557021,0.34689263334132264), (-2.331742795642637,-0.5771627038699307), (1.0060513913917988,-0.04827308490054824), (-0.4295021770429881,-0.8361201294738408), (-0.21561647198666445,0.2776346366703466), (1.4993086132360396,-2.583216199463966), (1.0627822777545926,-0.5754336242709439), (-2.4095942089088482,-0.4740298729504418), (1.5975461609152366,0.45528050967309863), (2.1565799558529477,-0.937001723779216), (0.0962289023611611,0.12442585999261868), (2.7095641996945012,-1.037597020742512), (-0.9219208363686942,0.6656497732259127), (-1.0338058740520197,0.09718535082910094), (1.6364174103842437,0.472044663569549), (1.2274838964756027,0.01365815985041524), (-0.7303973452135653,0.21443946294874028), (1.6471043795729972,0.38540036725618015), (0.13612088488197083,0.7807770362857364), (-4.201610226957669,0.12940125757456059), (0.8225909001565422,-0.9556204076037894), (-0.9482388322224697,-1.0007190699745476), (-1.5523717842885172,-1.4238233782457508), (-1.4048827543540963,0.09834207753278863), (1.5338217310158921,-2.5447136959911605), (2.2987508128497063,0.1997331013963162), (-1.9069233483255275,1.2806574080505437), (-0.48017389975436475,0.9315316175761184), (3.708171200382932,-0.6394107818742375), (1.646362242992558,-1.4099227738716835), (5.344457888650813,-0.21682467720141205), (4.0401870066710455,-0.214248285713938...
val rddLR = sc.parallelize(yx)
rddLR.take(5)
rddLR: org.apache.spark.rdd.RDD[(Double, Double, Double)] = ParallelCollectionRDD[4] at parallelize at command-2971213210277128:1
res2: Array[(Double, Double, Double)] = Array((5.677461671611596,1.6983876233282715,0.1510498720110515), (6.5963106438672625,1.9306937118935266,-0.21709023201809416), (-6.457080480280965,-3.613579039093624,-0.9420189342233366), (5.725199404014689,1.4774128627063305,-0.6050521762158899), (5.2485124653727215,0.49536920319327216,0.48771393128636853))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show
dfLR.show(5)
+-------------------+--------------------+--------------------+
| y| x1| x2|
+-------------------+--------------------+--------------------+
| 5.677461671611596| 1.6983876233282715| 0.1510498720110515|
| 6.5963106438672625| 1.9306937118935266|-0.21709023201809416|
| -6.457080480280965| -3.613579039093624| -0.9420189342233366|
| 5.725199404014689| 1.4774128627063305| -0.6050521762158899|
| 5.2485124653727215| 0.49536920319327216| 0.48771393128636853|
| 6.147100844408568| 2.619266385545484|0.027986752952643124|
|-0.7268042607303868| -1.6216138344745752| 0.08206698908429294|
| 5.5363351738375695| 1.2511380715559544| -0.7810931162704136|
|-0.1828379004896784| -0.6891022233658615| -1.2057806605697985|
| 6.488199218865876| 2.7890847095863527| -0.9176042669882863|
| 1.3969322930903634| -0.8384317817611013| 1.1959113751272468|
| 1.468434023004454|-0.39702300426389736| 0.5771223111098949|
|-0.6234341966751746|-0.20341459413042506| -0.5030579509564024|
| 6.452061108539848| 1.940354690386933| 0.05792512611966754|
| 9.199371261060582| 3.1686700336304794| 2.0492117764647917|
|0.07256979343692907|-0.01571825151138...| -1.6192400586380957|
| 9.589528428542355| 3.5222633033548227|-0.28568171141353893|
| 1.1696257215909478| -1.7700416261712641| 1.662660432138664|
|-0.8138719762638745| -1.276616299487233| -0.3053683539449641|
| 9.309926019422797| 2.847017612404211| 0.3879563181609168|
+-------------------+--------------------+--------------------+
only showing top 20 rows
+------------------+-------------------+--------------------+
| y| x1| x2|
+------------------+-------------------+--------------------+
| 5.677461671611596| 1.6983876233282715| 0.1510498720110515|
|6.5963106438672625| 1.9306937118935266|-0.21709023201809416|
|-6.457080480280965| -3.613579039093624| -0.9420189342233366|
| 5.725199404014689| 1.4774128627063305| -0.6050521762158899|
|5.2485124653727215|0.49536920319327216| 0.48771393128636853|
+------------------+-------------------+--------------------+
only showing top 5 rows
dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
// importing for regression
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
val lm = new LinearRegression
lm.explainParams
lm.getStandardization
lm.setStandardization(false)
lm.getStandardization
lm.explainParams
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
lm: org.apache.spark.ml.regression.LinearRegression = linReg_fd18c4adb9af
res5: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxBlockSizeInMB: Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0. (default: 0.0)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true, current: false)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
// Transform data frame to required format
val dflr = (dfLR map {row => (row.getDouble(0),
Vectors.dense(row.getDouble(1),row.getDouble(2)))}).
toDF("label","features")
dflr.show(5)
+------------------+--------------------+
| label| features|
+------------------+--------------------+
| 5.677461671611596|[1.69838762332827...|
|6.5963106438672625|[1.93069371189352...|
|-6.457080480280965|[-3.6135790390936...|
| 5.725199404014689|[1.47741286270633...|
|5.2485124653727215|[0.49536920319327...|
+------------------+--------------------+
only showing top 5 rows
dflr: org.apache.spark.sql.DataFrame = [label: double, features: vector]
// Fit model
val fit = lm.fit(dflr)
fit.intercept
fit: org.apache.spark.ml.regression.LinearRegressionModel = LinearRegressionModel: uid=linReg_fd18c4adb9af, numFeatures=2
res8: Double = 1.4942320912612896
fit.coefficients
res9: org.apache.spark.ml.linalg.Vector = [1.9996093571014764,1.0144075351786204]
val summ = fit.summary
summ: org.apache.spark.ml.regression.LinearRegressionTrainingSummary = org.apache.spark.ml.regression.LinearRegressionTrainingSummary@b34e84f
summ.r2
res10: Double = 0.9436414660418528
summ.rootMeanSquaredError
res11: Double = 1.0026971017612039
summ.coefficientStandardErrors
res12: Array[Double] = Array(0.005018897596822849, 0.009968316877946075, 0.01123197045538135)
summ.pValues
res13: Array[Double] = Array(0.0, 0.0, 0.0)
summ.tValues
res14: Array[Double] = Array(398.4160502432455, 101.76317101464718, 133.03383384038267)
summ.predictions.show(5)
+------------------+--------------------+------------------+
| label| features| prediction|
+------------------+--------------------+------------------+
| 5.677461671611596|[1.69838762332827...| 5.043570003209616|
|6.5963106438672625|[1.93069371189352...| 5.134647336087737|
|-6.457080480280965|[-3.6135790390936...|-6.687105473093168|
| 5.725199404014689|[1.47741286270633...| 3.834711189101326|
|5.2485124653727215|[0.49536920319327...|2.9795176720949392|
+------------------+--------------------+------------------+
only showing top 5 rows
summ.residuals.show(5)
+-------------------+
| residuals|
+-------------------+
| 0.6338916684019802|
| 1.4616633077795251|
|0.23002499281220334|
| 1.890488214913363|
| 2.2689947932777823|
+-------------------+
only showing top 5 rows
This gives you more on doing generalised linear modelling in Scala. But let's go back to out pipeline using the power-plant data.
Exercise: Writing your own Spark program for the least squares fit
Your task is to use the syntactic sugar below to write your own linear regression function using reduce and broadcast operations
How would you write your own Spark program to find the least squares fit on the following 1000 data points in RDD rddLR, where the variable y is the response variable, and X1 and X2 are independent variables?
More precisely, find \(w_1, w_2\), such that,
\(\sum_{i=1}^{10} (w_1 X1_i + w_2 X2_i - y_i)^2\) is minimized.
Report \(w_1\), \(w_2\), and the Root Mean Square Error and submit code in Spark. Analyze the resulting algorithm in terms of all-to-all, one-to-all, and all-to-one communication patterns.
rddLR.count
res17: Long = 10000
rddLR.take(10)
res18: Array[(Double, Double, Double)] = Array((5.677461671611596,1.6983876233282715,0.1510498720110515), (6.5963106438672625,1.9306937118935266,-0.21709023201809416), (-6.457080480280965,-3.613579039093624,-0.9420189342233366), (5.725199404014689,1.4774128627063305,-0.6050521762158899), (5.2485124653727215,0.49536920319327216,0.48771393128636853), (6.147100844408568,2.619266385545484,0.027986752952643124), (-0.7268042607303868,-1.6216138344745752,0.08206698908429294), (5.5363351738375695,1.2511380715559544,-0.7810931162704136), (-0.1828379004896784,-0.6891022233658615,-1.2057806605697985), (6.488199218865876,2.7890847095863527,-0.9176042669882863))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show(10)
+-------------------+-------------------+--------------------+
| y| x1| x2|
+-------------------+-------------------+--------------------+
| 5.677461671611596| 1.6983876233282715| 0.1510498720110515|
| 6.5963106438672625| 1.9306937118935266|-0.21709023201809416|
| -6.457080480280965| -3.613579039093624| -0.9420189342233366|
| 5.725199404014689| 1.4774128627063305| -0.6050521762158899|
| 5.2485124653727215|0.49536920319327216| 0.48771393128636853|
| 6.147100844408568| 2.619266385545484|0.027986752952643124|
|-0.7268042607303868|-1.6216138344745752| 0.08206698908429294|
| 5.5363351738375695| 1.2511380715559544| -0.7810931162704136|
|-0.1828379004896784|-0.6891022233658615| -1.2057806605697985|
| 6.488199218865876| 2.7890847095863527| -0.9176042669882863|
+-------------------+-------------------+--------------------+
only showing top 10 rows
dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
rddLR.getNumPartitions
res21: Int = 2
rddLR.map( yx1x2 => (yx1x2._2, yx1x2._3, yx1x2._1) ).take(10)
res22: Array[(Double, Double, Double)] = Array((1.6983876233282715,0.1510498720110515,5.677461671611596), (1.9306937118935266,-0.21709023201809416,6.5963106438672625), (-3.613579039093624,-0.9420189342233366,-6.457080480280965), (1.4774128627063305,-0.6050521762158899,5.725199404014689), (0.49536920319327216,0.48771393128636853,5.2485124653727215), (2.619266385545484,0.027986752952643124,6.147100844408568), (-1.6216138344745752,0.08206698908429294,-0.7268042607303868), (1.2511380715559544,-0.7810931162704136,5.5363351738375695), (-0.6891022233658615,-1.2057806605697985,-0.1828379004896784), (2.7890847095863527,-0.9176042669882863,6.488199218865876))
import breeze.linalg.DenseVector
val pts = rddLR.map( yx1x2 => DenseVector(yx1x2._2, yx1x2._3, yx1x2._1) ).cache
import breeze.linalg.DenseVector
pts: org.apache.spark.rdd.RDD[breeze.linalg.DenseVector[Double]] = MapPartitionsRDD[40] at map at command-2971213210277150:2
Now all we need to do is one-to-all and all-to-one broadcast of the gradient...
val w = DenseVector(0.0, 0.0, 0.0)
val w_bc = sc.broadcast(w)
val step = 0.1
val max_iter = 100
/*
YouTry: Fix the expressions for grad_w0, grad_w1 and grad_w2 to have w_bc.value be the same as that from ml lib's fit.coefficients
*/
for (i <- 1 to max_iter) {
val grad_w0 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*1.0).reduce(_+_)
val grad_w1 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(0)).reduce(_+_)
val grad_w2 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(1)).reduce(_+_)
w_bc.value(0) = w_bc.value(0) - step*grad_w0
w_bc.value(1) = w_bc.value(1) - step*grad_w1
w_bc.value(2) = w_bc.value(2) - step*grad_w2
}
w: breeze.linalg.DenseVector[Double] = DenseVector(703602.5501372493, 2297216.693185762, 194396.78259181185)
w_bc: org.apache.spark.broadcast.Broadcast[breeze.linalg.DenseVector[Double]] = Broadcast(16)
step: Double = 0.1
max_iter: Int = 100
w_bc.value
res24: breeze.linalg.DenseVector[Double] = DenseVector(703602.5501372493, 2297216.693185762, 194396.78259181185)
fit.intercept
res25: Double = 1.4942320912612896
fit.coefficients
res26: org.apache.spark.ml.linalg.Vector = [1.9996093571014764,1.0144075351786204]
Computing each of the gradients requires an all-to-one communication (due to the .reduce(_+_)). There are two of these per iteration. Broadcasting the updated w_bc requires one to all communication.
A more detailed deep dive
We will mostly be using ML algorithms already implemented in Spark's libraries and packages.
However, in order to innovate and devise new algorithms, we need to understand more about distributed linear algebra and optimisation algorithms.
- read: http://arxiv.org/pdf/1509.02256.pdf (also see References and Appendix A).
- and go through the notebooks prepended by '19x' on various Data Types for distributed linear algebra.
You may want to follow this course from Stanford for a guided deeper dive. * http://stanford.edu/~rezab/dao/ * see notes here: https://github.com/lamastex/scalable-data-science/blob/master/read/daosu.pdf
Communication Hierarchy
In addition to time and space complexity of an algorithm, in the distributed setting, we also need to take care of communication complexity.
Access rates fall sharply with distance.
- roughly 50 x gap between reading from memory and reading from either disk or the network.
We must take this communication hierarchy into consideration when developing parallel and distributed algorithms.
Focusing on strategies to reduce communication costs.
- access rates fall sharply with distance.
- so this communication hierarchy needs to be accounted for when developing parallel and distributed algorithms.
Lessons:
-
parallelism makes our computation faster
-
but network communication slows us down
-
SOLUTION: perform parallel and in-memory computation.
-
Persisting in memory is a particularly attractive option when working with iterative algorithms that read the same data multiple times, as is the case in gradient descent.
-
Several machine learning algorithms are iterative!
-
Limits of multi-core scaling (powerful multicore machine with several CPUs, and a huge amount of RAM).
- advantageous:
- sidestep any network communication when working with a single multicore machine
- can indeed handle fairly large data sets, and they're an attractive option in many settings.
- disadvantages:
- can be quite expensive (due to specialized hardware),
- not as widely accessible as commodity computing nodes.
- this approach does have scalability limitations, as we'll eventually hit a wall when the data grows large enough! This is not the case for a distributed environment (like the AWS EC2 cloud under the hood here).
- advantageous:
Simple strategies for algorithms in a distributed setting: to reduce network communication, simply keep large objects local
- In the big n, small d case for linear regression
- we can solve the problem via a closed form solution.
- And this requires us to communicate \(O(d)^2\) intermediate data.
- the largest object in this example is our initial data, which we store in a distributed fashion and never communicate! This is a data parallel setting.
- In the big n, big d case (n is the sample size and d is the number of features):
- for linear regression.
- we use gradient descent to iteratively train our model and are again in a data parallel setting.
- At each iteration we communicate the current parameter vector \(w_i\) and the required \(O(d)\) communication is feasible even for fairly large d.
- for linear regression.
- In the small n, small d case:
- for ridge regression.
- we can communicate the small data to all of the workers.
- this is an example of a model parallel setting where we can train the model for each hyper-parameter in parallel.
- for ridge regression.
- Linear regression with big n and huge d is an example of both data and model parallelism.
In this setting, since our data is large, we must still store it across multiple machines. We can still use gradient descent, or stochastic variants of gradient descent to train our model, but we may not want to communicate the entire d dimensional parameter vector at each iteration, when we have 10s, or hundreds of millions of features. In this setting we often rely on sparsity to reduce the communication. So far we discussed how we can reduce communication by keeping large data local.
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Why are we going through the basic distributed linear algebra Data Types?
This is to get you to be able to directly use them in a project or to roll your own algorithms.
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Local vector in Scala
A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine.
MLlib supports two types of local vectors:
- dense and
- sparse.
A dense vector is backed by a double array representing its entry values, while a sparse vector is backed by two parallel arrays: indices and values.
For example, a vector (1.0, 0.0, 3.0) can be represented:
- in dense format as
[1.0, 0.0, 3.0]or - in sparse format as
(3, [0, 2], [1.0, 3.0]), where3is the size of the vector.
The base class of local vectors is Vector, and we provide two implementations: DenseVector and SparseVector. We recommend using the factory methods implemented in Vectors to create local vectors. Refer to the Vector Scala docs and Vectors Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
// Create a dense vector (1.0, 0.0, 3.0).
val dv: Vector = Vectors.dense(1.0, 0.0, 3.0)
import org.apache.spark.mllib.linalg.{Vector, Vectors}
dv: org.apache.spark.mllib.linalg.Vector = [1.0,0.0,3.0]
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.
val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))
sv1: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.
val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))
sv2: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
Note: Scala imports scala.collection.immutable.Vector by default, so you have to import org.apache.spark.mllib.linalg.Vector explicitly to use MLlib’s Vector.
python: MLlib recognizes the following types as dense vectors:
- NumPy’s
array - Python’s list, e.g.,
[1, 2, 3]
and the following as sparse vectors:
- MLlib’s
SparseVector. - SciPy’s
csc_matrixwith a single column
We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented in Vectors to create sparse vectors.
Refer to the Vectors Python docs for more details on the API.
import numpy as np
import scipy.sparse as sps
from pyspark.mllib.linalg import Vectors
# Use a NumPy array as a dense vector.
dv1 = np.array([1.0, 0.0, 3.0])
# Use a Python list as a dense vector.
dv2 = [1.0, 0.0, 3.0]
# Create a SparseVector.
sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0])
# Use a single-column SciPy csc_matrix as a sparse vector.
sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape = (3, 1))
print (dv1)
print (dv2)
print (sv1)
print (sv2)
[1. 0. 3.]
[1.0, 0.0, 3.0]
(3,[0,2],[1.0,3.0])
(0, 0) 1.0
(2, 0) 3.0
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Labeled point in Scala
A labeled point is a local vector, either dense or sparse, associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms.
We use a double to store a label, so we can use labeled points in both regression and classification.
For binary classification, a label should be either 0 (negative) or 1 (positive). For multiclass classification, labels should be class indices starting from zero: 0, 1, 2, ....
A labeled point is represented by the case class LabeledPoint.
Refer to the LabeledPoint Scala docs for details on the API.
//import first
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
// Create a labeled point with a "positive" label and a dense feature vector.
val pos = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0))
pos: org.apache.spark.mllib.regression.LabeledPoint = (1.0,[1.0,0.0,3.0])
// Create a labeled point with a "negative" label and a sparse feature vector.
val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
neg: org.apache.spark.mllib.regression.LabeledPoint = (0.0,(3,[0,2],[1.0,3.0]))
Sparse data in Scala
It is very common in practice to have sparse training data. MLlib supports reading training examples stored in LIBSVM format, which is the default format used by LIBSVM and LIBLINEAR. It is a text format in which each line represents a labeled sparse feature vector using the following format:
label index1:value1 index2:value2 ...
where the indices are one-based and in ascending order. After loading, the feature indices are converted to zero-based.
MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.
Refer to the MLUtils Scala docs for details on the API.
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
//val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") // from prog guide but no such data here - can wget from github
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
Load MNIST training and test datasets
Our datasets are vectors of pixels representing images of handwritten digits. For example:

display(dbutils.fs.ls("/datasets/sds/mnist-digits/data-001/mnist-digits-train.txt"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/sds/mnist-digits/data-001/mnist-digits-train.txt | mnist-digits-train.txt | 6.9430283e7 |
val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "/datasets/sds/mnist-digits/data-001/mnist-digits-train.txt")
examples: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[246] at map at MLUtils.scala:87
examples.take(1)
res7: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))
Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).
display(examples.toDF) // covert to DataFrame and display for convenient db visualization
The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) below, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):
type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]
Here
type: 0says we hve a sparse vector.size: 780says the vector has 780 indices in total- these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
indices: [152,153,155,...,682,683]are the indices from the[0,1,...,779]possible indices with non-zero values- a value is an integer encoding the gray-level at the pixel index
values: [3, 18, 18,18,126,...,132,16]are the actual gray level values, for example:- at pixed index
152the gray-level value is3, - at index
153the gray-level value is18, - ..., and finally at
- at index
683the gray-level value is18
- at pixed index
We could also use the following method as done in notebook 016_* already.
val training = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/datasets/sds/mnist-digits/data-001/mnist-digits-train.txt")
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
display(training)
Labeled point in Python
A labeled point is represented by LabeledPoint.
Refer to the LabeledPoint Python docs for more details on the API.
# import first
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint
# Create a labeled point with a positive label and a dense feature vector.
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])
# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
Sparse data in Python
MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.
Refer to the MLUtils Python docs for more details on the API.
from pyspark.mllib.util import MLUtils
# examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") #from prog guide but no such data here - can wget from github
examples = MLUtils.loadLibSVMFile(sc, "/datasets/sds/mnist-digits/data-001/mnist-digits-train.txt")
examples.take(1)
res11: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Local Matrix in Scala
A local matrix has integer-typed row and column indices and double-typed values, stored on a single machine. MLlib supports:
- dense matrices, whose entry values are stored in a single double array in column-major order, and
- sparse matrices, whose non-zero entry values are stored in the Compressed Sparse Column (CSC) format in column-major order.
For example, the following dense matrix: \[ \begin{pmatrix} 1.0 & 2.0 \\ 3.0 & 4.0 \\ 5.0 & 6.0 \end{pmatrix} \] is stored in a one-dimensional array [1.0, 3.0, 5.0, 2.0, 4.0, 6.0] with the matrix size (3, 2).
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
Refer to the Matrix Scala docs and Matrices Scala docs for details on the API.
Int.MaxValue // note the largest value an index can take
res0: Int = 2147483647
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
dm: org.apache.spark.mllib.linalg.Matrix =
1.0 2.0
3.0 4.0
5.0 6.0
Next, let us create the following sparse local matrix: \[ \begin{pmatrix} 9.0 & 0.0 \\ 0.0 & 8.0 \\ 0.0 & 6.0 \end{pmatrix} \]
// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))
sm: org.apache.spark.mllib.linalg.Matrix =
3 x 2 CSCMatrix
(0,0) 9.0
(2,1) 6.0
(1,1) 8.0
Local Matrix in Python
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
Refer to the Matrix Python docs and Matrices Python docs for more details on the API.
from pyspark.mllib.linalg import Matrix, Matrices
# Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
dm2
# Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
sm
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Distributed matrix in Scala
A distributed matrix has long-typed row and column indices and double-typed values, stored distributively in one or more RDDs.
It is very important to choose the right format to store large and distributed matrices. Converting a distributed matrix to a different format may require a global shuffle, which is quite expensive.
Three types of distributed matrices have been implemented so far.
- The basic type is called
RowMatrix.
- A
RowMatrixis a row-oriented distributed matrix without meaningful row indices, e.g., a collection of feature vectors. It is backed by an RDD of its rows, where each row is a local vector. - We assume that the number of columns is not huge for a
RowMatrixso that a single local vector can be reasonably communicated to the driver and can also be stored / operated on using a single node. - An
IndexedRowMatrixis similar to aRowMatrixbut with row indices, which can be used for identifying rows and executing joins. - A
CoordinateMatrixis a distributed matrix stored in coordinate list (COO) format, backed by an RDD of its entries.
Note
The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general the use of non-deterministic RDDs can lead to errors.
Remark: there is a huge difference in the orders of magnitude between the maximum size of local versus distributed matrices!
print(Long.MaxValue.toDouble, Int.MaxValue.toDouble, Long.MaxValue.toDouble / Int.MaxValue.toDouble) // index ranges and ratio for local and distributed matrices
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
RowMatrix in Scala
A RowMatrix is a row-oriented distributed matrix without meaningful row indices, backed by an RDD of its rows, where each row is a local vector. Since each row is represented by a local vector, the number of columns is limited by the integer range but it should be much smaller in practice.
A RowMatrix can be created from an RDD[Vector] instance. Then we can compute its column summary statistics and decompositions.
- QR decomposition is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
- For singular value decomposition (SVD) and principal component analysis (PCA), please refer to Dimensionality reduction.
Refer to the RowMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
val rows: RDD[Vector] = sc.parallelize(Array(Vectors.dense(12.0, -51.0, 4.0), Vectors.dense(6.0, 167.0, -68.0), Vectors.dense(-4.0, 24.0, -41.0))) // an RDD of local vectors
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector] = ParallelCollectionRDD[3670] at parallelize at command-2972105651606776:1
// Create a RowMatrix from an RDD[Vector].
val mat: RowMatrix = new RowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@75c66624
mat.rows.collect
res0: Array[org.apache.spark.mllib.linalg.Vector] = Array([12.0,-51.0,4.0], [6.0,167.0,-68.0], [-4.0,24.0,-41.0])
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 3
n: Long = 3
// QR decomposition
val qrResult = mat.tallSkinnyQR(true)
qrResult: org.apache.spark.mllib.linalg.QRDecomposition[org.apache.spark.mllib.linalg.distributed.RowMatrix,org.apache.spark.mllib.linalg.Matrix] =
QRDecomposition(org.apache.spark.mllib.linalg.distributed.RowMatrix@34f5da4f,14.0 21.0 -14.0
0.0 -174.99999999999997 70.00000000000001
0.0 0.0 -35.000000000000014 )
qrResult.R
res1: org.apache.spark.mllib.linalg.Matrix =
14.0 21.0 -14.0
0.0 -174.99999999999997 70.00000000000001
0.0 0.0 -35.000000000000014
RowMatrix in Python
A RowMatrix can be created from an RDD of vectors.
Refer to the RowMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import RowMatrix
# Create an RDD of vectors.
rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
# Create a RowMatrix from an RDD of vectors.
mat = RowMatrix(rows)
# Get its size.
m = mat.numRows() # 4
n = mat.numCols() # 3
print (m,'x',n)
# Get the rows as an RDD of vectors again.
rowsRDD = mat.rows
4 x 3
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
IndexedRowMatrix in Scala
An IndexedRowMatrix is similar to a RowMatrix but with meaningful row indices. It is backed by an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector.
An IndexedRowMatrix can be created from an RDD[IndexedRow] instance, where IndexedRow is a wrapper over (Long, Vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.
Refer to the IndexedRowMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
Vector(12.0, -51.0, 4.0) // note Vector is a scala collection
res0: scala.collection.immutable.Vector[Double] = Vector(12.0, -51.0, 4.0)
Vectors.dense(12.0, -51.0, 4.0) // while this is a mllib.linalg.Vector
res1: org.apache.spark.mllib.linalg.Vector = [12.0,-51.0,4.0]
val rows: RDD[IndexedRow] = sc.parallelize(Array(IndexedRow(2, Vectors.dense(1,3)), IndexedRow(4, Vectors.dense(4,5)))) // an RDD of indexed rows
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.IndexedRow] = ParallelCollectionRDD[3685] at parallelize at command-2972105651607186:1
// Create an IndexedRowMatrix from an RDD[IndexedRow].
val mat: IndexedRowMatrix = new IndexedRowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@300e1e99
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 5
n: Long = 2
// Drop its row indices.
val rowMat: RowMatrix = mat.toRowMatrix()
rowMat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@425fc4a2
rowMat.rows.collect()
res2: Array[org.apache.spark.mllib.linalg.Vector] = Array([1.0,3.0], [4.0,5.0])
IndexedRowMatrix in Python
An IndexedRowMatrix can be created from an RDD of IndexedRows, where IndexedRow is a wrapper over (long, vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.
Refer to the IndexedRowMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
# Create an RDD of indexed rows.
# - This can be done explicitly with the IndexedRow class:
indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
IndexedRow(1, [4, 5, 6]),
IndexedRow(2, [7, 8, 9]),
IndexedRow(3, [10, 11, 12])])
# - or by using (long, vector) tuples:
indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]),
(2, [7, 8, 9]), (3, [10, 11, 12])])
# Create an IndexedRowMatrix from an RDD of IndexedRows.
mat = IndexedRowMatrix(indexedRows)
# Get its size.
m = mat.numRows() # 4
n = mat.numCols() # 3
print (m,n)
# Get the rows as an RDD of IndexedRows.
rowsRDD = mat.rows
# Convert to a RowMatrix by dropping the row indices.
rowMat = mat.toRowMatrix()
# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
4 3
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
CoordinateMatrix in Scala
A CoordinateMatrix is a distributed matrix backed by an RDD of its entries. Each entry is a tuple of (i: Long, j: Long, value: Double), where i is the row index, j is the column index, and value is the entry value. A CoordinateMatrix should be used only when both dimensions of the matrix are huge and the matrix is very sparse.
A CoordinateMatrix can be created from an RDD[MatrixEntry] instance, where MatrixEntry is a wrapper over (Long, Long, Double). A CoordinateMatrix can be converted to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix. Other computations for CoordinateMatrix are not currently supported.
Refer to the CoordinateMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3712] at parallelize at command-2972105651606627:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val mat: CoordinateMatrix = new CoordinateMatrix(entries)
mat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@56954954
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 7
n: Long = 2
// Convert it to an IndexRowMatrix whose rows are sparse vectors.
val indexedRowMatrix = mat.toIndexedRowMatrix()
indexedRowMatrix: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@45570b82
indexedRowMatrix.rows.collect()
res0: Array[org.apache.spark.mllib.linalg.distributed.IndexedRow] = Array(IndexedRow(0,(2,[0],[1.2])), IndexedRow(1,(2,[0],[2.1])), IndexedRow(6,(2,[1],[3.7])))
CoordinateMatrix in Scala
A CoordinateMatrix can be created from an RDD of MatrixEntry entries, where MatrixEntry is a wrapper over (long, long, float). A CoordinateMatrix can be converted to a RowMatrix by calling toRowMatrix, or to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix.
Refer to the CoordinateMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
# Create an RDD of coordinate entries.
# - This can be done explicitly with the MatrixEntry class:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])
# - or using (long, long, float) tuples:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])
# Create an CoordinateMatrix from an RDD of MatrixEntries.
mat = CoordinateMatrix(entries)
# Get its size.
m = mat.numRows() # 3
n = mat.numCols() # 2
print (m,n)
# Get the entries as an RDD of MatrixEntries.
entriesRDD = mat.entries
# Convert to a RowMatrix.
rowMat = mat.toRowMatrix()
# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()
# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
3 2
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
BlockMatrix in Scala
A BlockMatrix is a distributed matrix backed by an RDD of MatrixBlocks, where a MatrixBlock is a tuple of ((Int, Int), Matrix), where the (Int, Int) is the index of the block, and Matrix is the sub-matrix at the given index with size rowsPerBlock x colsPerBlock. BlockMatrix supports methods such as add and multiply with another BlockMatrix. BlockMatrix also has a helper function validate which can be used to check whether the BlockMatrix is set up properly.
A BlockMatrix can be most easily created from an IndexedRowMatrix or CoordinateMatrix by calling toBlockMatrix. toBlockMatrix creates blocks of size 1024 x 1024 by default. Users may change the block size by supplying the values through toBlockMatrix(rowsPerBlock, colsPerBlock).
Refer to the BlockMatrix Scala docs for details on the API.
//import org.apache.spark.mllib.linalg.{Matrix, Matrices}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3746] at parallelize at command-2972105651607062:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val coordMat: CoordinateMatrix = new CoordinateMatrix(entries)
coordMat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@161a1942
// Transform the CoordinateMatrix to a BlockMatrix
val matA: BlockMatrix = coordMat.toBlockMatrix().cache()
matA: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@1ddf85ce
// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid.
// Nothing happens if it is valid.
matA.validate()
// Calculate A^T A.
val ata = matA.transpose.multiply(matA)
ata: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@18c0eeca
ata.blocks.collect()
res1: Array[((Int, Int), org.apache.spark.mllib.linalg.Matrix)] =
Array(((0,0),5.85 0.0
0.0 13.690000000000001 ))
ata.toLocalMatrix()
res2: org.apache.spark.mllib.linalg.Matrix =
5.85 0.0
0.0 13.690000000000001
BlockMatrix in Scala
A BlockMatrix can be created from an RDD of sub-matrix blocks, where a sub-matrix block is a ((blockRowIndex, blockColIndex), sub-matrix) tuple.
Refer to the BlockMatrix Python docs for more details on the API.
from pyspark.mllib.linalg import Matrices
from pyspark.mllib.linalg.distributed import BlockMatrix
# Create an RDD of sub-matrix blocks.
blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
# Create a BlockMatrix from an RDD of sub-matrix blocks.
mat = BlockMatrix(blocks, 3, 2)
# Get its size.
m = mat.numRows() # 6
n = mat.numCols() # 2
print (m,n)
# Get the blocks as an RDD of sub-matrix blocks.
blocksRDD = mat.blocks
# Convert to a LocalMatrix.
localMat = mat.toLocalMatrix()
# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()
# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
6 2
Power Plant ML Pipeline Application
This is an end-to-end example of using a number of different machine learning algorithms to solve a supervised regression problem.
Table of Contents
- Step 1: Business Understanding
- Step 2: Load Your Data
- Step 3: Explore Your Data
- Step 4: Visualize Your Data
- Step 5: Data Preparation
- Step 6: Data Modeling
- Step 7: Tuning and Evaluation
- Step 8: Deployment
We are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant. Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid.
More information about Peaker or Peaking Power Plants can be found on Wikipedia https://en.wikipedia.org/wiki/Peakingpowerplant
Given this business problem, we need to translate it to a Machine Learning task. The ML task is regression since the label (or target) we are trying to predict is numeric.
The example data is provided by UCI at UCI Machine Learning Repository Combined Cycle Power Plant Data Set
You can read the background on the UCI page, but in summary we have collected a number of readings from sensors at a Gas Fired Power Plant
(also called a Peaker Plant) and now we want to use those sensor readings to predict how much power the plant will generate.
More information about Machine Learning with Spark can be found in the Spark MLLib Programming Guide
Please note this example only works with Spark version 1.4 or higher
To Rerun Steps 1-4 done in the notebook at:
Workspace -> PATH_TO -> 009_PowerPlantPipeline_01ETLEDA]
just run the following command as shown in the cell below:
%run "/PATH_TO/009_PowerPlantPipeline_01ETLEDA"
-
Note: If you already evaluated the
%run ...command above then:- first delete the cell by pressing on
xon the top-right corner of the cell and - revaluate the
runcommand above.
- first delete the cell by pressing on
"../009_PowerPlantPipeline_01ETLEDA"
Now we will do the following Steps:
Step 5: Data Preparation,
Step 6: Modeling, and
Step 7: Tuning and Evaluation
We will do Step 8: Deployment later after we get introduced to SparkStreaming.
Step 5: Data Preparation
The next step is to prepare the data. Since all of this data is numeric and consistent, this is a simple task for us today.
We will need to convert the predictor features from columns to Feature Vectors using the org.apache.spark.ml.feature.VectorAssembler
The VectorAssembler will be the first step in building our ML pipeline.
res2: Int = 301
//Let's quickly recall the schema and make sure our table is here now
table("power_plant_table").printSchema
root
|-- AT: double (nullable = true)
|-- V: double (nullable = true)
|-- AP: double (nullable = true)
|-- RH: double (nullable = true)
|-- PE: double (nullable = true)
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/power-plant/data/Sheet1.tsv | Sheet1.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet2.tsv | Sheet2.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet3.tsv | Sheet3.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet4.tsv | Sheet4.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet5.tsv | Sheet5.tsv | 308693.0 |
powerPlantRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/power-plant/data/Sheet1.tsv MapPartitionsRDD[1] at textFile at command-1267216879634554:1
powerPlantDF // make sure we have the DataFrame too
res82: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
powerPlantDF: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
root
|-- AT: double (nullable = true)
|-- V: double (nullable = true)
|-- AP: double (nullable = true)
|-- RH: double (nullable = true)
|-- PE: double (nullable = true)
import org.apache.spark.ml.feature.VectorAssembler
// make a DataFrame called dataset from the table
val dataset = sqlContext.table("power_plant_table")
val vectorizer = new VectorAssembler()
.setInputCols(Array("AT", "V", "AP", "RH"))
.setOutputCol("features")
import org.apache.spark.ml.feature.VectorAssembler
dataset: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
vectorizer: org.apache.spark.ml.feature.VectorAssembler = VectorAssembler: uid=vecAssembler_ea4ed428c7e4, handleInvalid=error, numInputCols=4
res9: Long = 9568
+-----+-----+-------+-----+------+
| AT| V| AP| RH| PE|
+-----+-----+-------+-----+------+
|14.96|41.76|1024.07|73.17|463.26|
|25.18|62.96|1020.04|59.08|444.37|
| 5.11| 39.4|1012.16|92.14|488.56|
|20.86|57.32|1010.24|76.64|446.48|
|10.82| 37.5|1009.23|96.62| 473.9|
|26.27|59.44|1012.23|58.77|443.67|
|15.89|43.96|1014.02|75.24|467.35|
| 9.48|44.71|1019.12|66.43|478.42|
|14.64| 45.0|1021.78|41.25|475.98|
|11.74|43.56|1015.14|70.72| 477.5|
+-----+-----+-------+-----+------+
only showing top 10 rows
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
res12: Long = 9568
+--------+--------------------+-----------+
|database| tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...| false|
| default| sentimentlex_csv| false|
| default| simple_range| false|
| default| social_media_usage| false|
| default|social_media_usag...| false|
+--------+--------------------+-----------+
+------------------------------------------------------------+--------+-----------+---------+-----------+
|name |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|power_plant_predictions |default |null |MANAGED |false |
|sentimentlex_csv |default |null |EXTERNAL |false |
|simple_range |default |null |MANAGED |false |
|social_media_usage |default |null |MANAGED |false |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null |MANAGED |false |
+------------------------------------------------------------+--------+-----------+---------+-----------+
+-------+---------------------+-------------------------+
|name |description |locationUri |
+-------+---------------------+-------------------------+
|default|Default Hive database|dbfs:/user/hive/warehouse|
+-------+---------------------+-------------------------+
Step 6: Data Modeling
Now let's model our data to predict what the power output will be given a set of sensor readings
Our first model will be based on simple linear regression since we saw some linear patterns in our data based on the scatter plots during the exploration stage.
+--------+--------------------+-----------+
|database| tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...| false|
| default| sentimentlex_csv| false|
| default| simple_range| false|
| default| social_media_usage| false|
| default|social_media_usag...| false|
| | power_plant_table| true|
+--------+--------------------+-----------+
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
| col_name | data_type | comment |
|---|---|---|
| AT | double | null |
| V | double | null |
| AP | double | null |
| RH | double | null |
| PE | double | null |
| summary | AT | V | AP | RH | PE |
|---|---|---|---|---|---|
| count | 9568 | 9568 | 9568 | 9568 | 9568 |
| mean | 19.65123118729102 | 54.30580372073601 | 1013.2590781772603 | 73.30897784280926 | 454.3650094063554 |
| stddev | 7.4524732296110825 | 12.707892998326784 | 5.938783705811581 | 14.600268756728964 | 17.066994999803402 |
| min | 1.81 | 25.36 | 992.89 | 25.56 | 420.26 |
| max | 37.11 | 81.56 | 1033.3 | 100.16 | 495.76 |
| Temperature | Power |
|---|---|
| 14.96 | 463.26 |
| 25.18 | 444.37 |
| 5.11 | 488.56 |
| 20.86 | 446.48 |
| 10.82 | 473.9 |
| 26.27 | 443.67 |
| 15.89 | 467.35 |
| 9.48 | 478.42 |
| 14.64 | 475.98 |
| 11.74 | 477.5 |
| 17.99 | 453.02 |
| 20.14 | 453.99 |
| 24.34 | 440.29 |
| 25.71 | 451.28 |
| 26.19 | 433.99 |
| 21.42 | 462.19 |
| 18.21 | 467.54 |
| 11.04 | 477.2 |
| 14.45 | 459.85 |
| 13.97 | 464.3 |
| 17.76 | 468.27 |
| 5.41 | 495.24 |
| 7.76 | 483.8 |
| 27.23 | 443.61 |
| 27.36 | 436.06 |
| 27.47 | 443.25 |
| 14.6 | 464.16 |
| 7.91 | 475.52 |
| 5.81 | 484.41 |
| 30.53 | 437.89 |
| 23.87 | 445.11 |
| 26.09 | 438.86 |
| 29.27 | 440.98 |
| 27.38 | 436.65 |
| 24.81 | 444.26 |
| 12.75 | 465.86 |
| 24.66 | 444.37 |
| 16.38 | 450.69 |
| 13.91 | 469.02 |
| 23.18 | 448.86 |
| 22.47 | 447.14 |
| 13.39 | 469.18 |
| 9.28 | 482.8 |
| 11.82 | 476.7 |
| 10.27 | 474.99 |
| 22.92 | 444.22 |
| 16.0 | 461.33 |
| 21.22 | 448.06 |
| 13.46 | 474.6 |
| 9.39 | 473.05 |
| 31.07 | 432.06 |
| 12.82 | 467.41 |
| 32.57 | 430.12 |
| 8.11 | 473.62 |
| 13.92 | 471.81 |
| 23.04 | 442.99 |
| 27.31 | 442.77 |
| 5.91 | 491.49 |
| 25.26 | 447.46 |
| 27.97 | 446.11 |
| 26.08 | 442.44 |
| 29.01 | 446.22 |
| 12.18 | 471.49 |
| 13.76 | 463.5 |
| 25.5 | 440.01 |
| 28.26 | 441.03 |
| 21.39 | 452.68 |
| 7.26 | 474.91 |
| 10.54 | 478.77 |
| 27.71 | 434.2 |
| 23.11 | 437.91 |
| 7.51 | 477.61 |
| 26.46 | 431.65 |
| 29.34 | 430.57 |
| 10.32 | 481.09 |
| 22.74 | 445.56 |
| 13.48 | 475.74 |
| 25.52 | 435.12 |
| 21.58 | 446.15 |
| 27.66 | 436.64 |
| 26.96 | 436.69 |
| 12.29 | 468.75 |
| 15.86 | 466.6 |
| 13.87 | 465.48 |
| 24.09 | 441.34 |
| 20.45 | 441.83 |
| 15.07 | 464.7 |
| 32.72 | 437.99 |
| 18.23 | 459.12 |
| 35.56 | 429.69 |
| 18.36 | 459.8 |
| 26.35 | 433.63 |
| 25.92 | 442.84 |
| 8.01 | 485.13 |
| 19.63 | 459.12 |
| 20.02 | 445.31 |
| 10.08 | 480.8 |
| 27.23 | 432.55 |
| 23.37 | 443.86 |
| 18.74 | 449.77 |
| 14.81 | 470.71 |
| 23.1 | 452.17 |
| 10.72 | 478.29 |
| 29.46 | 428.54 |
| 8.1 | 478.27 |
| 27.29 | 439.58 |
| 17.1 | 457.32 |
| 11.49 | 475.51 |
| 23.69 | 439.66 |
| 13.51 | 471.99 |
| 9.64 | 479.81 |
| 25.65 | 434.78 |
| 21.59 | 446.58 |
| 27.98 | 437.76 |
| 18.8 | 459.36 |
| 18.28 | 462.28 |
| 13.55 | 464.33 |
| 22.99 | 444.36 |
| 23.94 | 438.64 |
| 13.74 | 470.49 |
| 21.3 | 455.13 |
| 27.54 | 450.22 |
| 24.81 | 440.43 |
| 4.97 | 482.98 |
| 15.22 | 460.44 |
| 23.88 | 444.97 |
| 33.01 | 433.94 |
| 25.98 | 439.73 |
| 28.18 | 434.48 |
| 21.67 | 442.33 |
| 17.67 | 457.67 |
| 21.37 | 454.66 |
| 28.69 | 432.21 |
| 16.61 | 457.66 |
| 27.91 | 435.21 |
| 20.97 | 448.22 |
| 10.8 | 475.51 |
| 20.61 | 446.53 |
| 25.45 | 441.3 |
| 30.16 | 433.54 |
| 4.99 | 472.52 |
| 10.51 | 474.77 |
| 33.79 | 435.1 |
| 21.34 | 450.74 |
| 23.4 | 442.7 |
| 32.21 | 426.56 |
| 14.26 | 463.71 |
| 27.71 | 447.06 |
| 21.95 | 452.27 |
| 25.76 | 445.78 |
| 23.68 | 438.65 |
| 8.28 | 480.15 |
| 23.44 | 447.19 |
| 25.32 | 443.04 |
| 3.94 | 488.81 |
| 17.3 | 455.75 |
| 18.2 | 455.86 |
| 21.43 | 457.68 |
| 11.16 | 479.11 |
| 30.38 | 432.84 |
| 23.36 | 448.37 |
| 21.69 | 447.06 |
| 23.62 | 443.53 |
| 21.87 | 445.21 |
| 29.25 | 441.7 |
| 20.03 | 450.93 |
| 18.14 | 451.44 |
| 24.23 | 441.29 |
| 18.11 | 458.85 |
| 6.57 | 481.46 |
| 12.56 | 467.19 |
| 13.4 | 461.54 |
| 27.1 | 439.08 |
| 14.28 | 467.22 |
| 16.29 | 468.8 |
| 31.24 | 426.93 |
| 10.57 | 474.65 |
| 13.8 | 468.97 |
| 25.3 | 433.97 |
| 18.06 | 450.53 |
| 25.42 | 444.51 |
| 15.07 | 469.03 |
| 11.75 | 466.56 |
| 20.23 | 457.57 |
| 27.31 | 440.13 |
| 28.57 | 433.24 |
| 17.9 | 452.55 |
| 23.83 | 443.29 |
| 27.92 | 431.76 |
| 17.34 | 454.97 |
| 17.94 | 456.7 |
| 6.4 | 486.03 |
| 11.78 | 472.79 |
| 20.28 | 452.03 |
| 21.04 | 443.41 |
| 25.11 | 441.93 |
| 30.28 | 432.64 |
| 8.14 | 480.25 |
| 16.86 | 466.68 |
| 6.25 | 494.39 |
| 22.35 | 454.72 |
| 17.98 | 448.71 |
| 21.19 | 469.76 |
| 20.94 | 450.71 |
| 24.23 | 444.01 |
| 19.18 | 453.2 |
| 20.88 | 450.87 |
| 23.67 | 441.73 |
| 14.12 | 465.09 |
| 25.23 | 447.28 |
| 6.54 | 491.16 |
| 20.08 | 450.98 |
| 24.67 | 446.3 |
| 27.82 | 436.48 |
| 15.55 | 460.84 |
| 24.26 | 442.56 |
| 13.45 | 467.3 |
| 11.06 | 479.13 |
| 24.91 | 441.15 |
| 22.39 | 445.52 |
| 11.95 | 475.4 |
| 14.85 | 469.3 |
| 10.11 | 463.57 |
| 23.67 | 445.32 |
| 16.14 | 461.03 |
| 15.11 | 466.74 |
| 24.14 | 444.04 |
| 30.08 | 434.01 |
| 14.77 | 465.23 |
| 27.6 | 440.6 |
| 13.89 | 466.74 |
| 26.85 | 433.48 |
| 12.41 | 473.59 |
| 13.08 | 474.81 |
| 18.93 | 454.75 |
| 20.5 | 452.94 |
| 30.72 | 435.83 |
| 7.55 | 482.19 |
| 13.49 | 466.66 |
| 15.62 | 462.59 |
| 24.8 | 447.82 |
| 10.03 | 462.73 |
| 22.43 | 447.98 |
| 14.95 | 462.72 |
| 24.78 | 442.42 |
| 23.2 | 444.69 |
| 14.01 | 466.7 |
| 19.4 | 453.84 |
| 30.15 | 436.92 |
| 6.91 | 486.37 |
| 29.04 | 440.43 |
| 26.02 | 446.82 |
| 5.89 | 484.91 |
| 26.52 | 437.76 |
| 28.53 | 438.91 |
| 16.59 | 464.19 |
| 22.95 | 442.19 |
| 23.96 | 446.86 |
| 17.48 | 457.15 |
| 6.69 | 482.57 |
| 10.25 | 476.03 |
| 28.87 | 428.89 |
| 12.04 | 472.7 |
| 22.58 | 445.6 |
| 15.12 | 464.78 |
| 25.48 | 440.42 |
| 27.87 | 428.41 |
| 23.72 | 438.5 |
| 25.0 | 438.28 |
| 8.42 | 476.29 |
| 22.46 | 448.46 |
| 29.92 | 438.99 |
| 11.68 | 471.8 |
| 14.04 | 471.81 |
| 19.86 | 449.82 |
| 25.99 | 442.14 |
| 23.42 | 441.46 |
| 10.6 | 477.62 |
| 20.97 | 446.76 |
| 14.14 | 472.52 |
| 8.56 | 471.58 |
| 24.86 | 440.85 |
| 29.0 | 431.37 |
| 27.59 | 437.33 |
| 10.45 | 469.22 |
| 8.51 | 471.11 |
| 29.82 | 439.17 |
| 22.56 | 445.33 |
| 11.38 | 473.71 |
| 20.25 | 452.66 |
| 22.42 | 440.99 |
| 14.85 | 467.42 |
| 25.62 | 444.14 |
| 19.85 | 457.17 |
| 13.67 | 467.87 |
| 24.39 | 442.04 |
| 16.07 | 471.36 |
| 11.6 | 460.7 |
| 31.38 | 431.33 |
| 29.91 | 432.6 |
| 19.67 | 447.61 |
| 27.18 | 443.87 |
| 21.39 | 446.87 |
| 10.45 | 465.74 |
| 19.46 | 447.86 |
| 23.55 | 447.65 |
| 23.35 | 437.87 |
| 9.26 | 483.51 |
| 10.3 | 479.65 |
| 20.94 | 455.16 |
| 23.13 | 431.91 |
| 12.77 | 470.68 |
| 28.29 | 429.28 |
| 19.13 | 450.81 |
| 24.44 | 437.73 |
| 20.32 | 460.21 |
| 20.54 | 442.86 |
| 12.16 | 482.99 |
| 28.09 | 440.0 |
| 9.25 | 478.48 |
| 21.75 | 455.28 |
| 23.7 | 436.94 |
| 16.22 | 461.06 |
| 24.75 | 438.28 |
| 10.48 | 472.61 |
| 29.53 | 426.85 |
| 12.59 | 470.18 |
| 23.5 | 455.38 |
| 29.01 | 428.32 |
| 9.75 | 480.35 |
| 19.55 | 455.56 |
| 21.05 | 447.66 |
| 24.72 | 443.06 |
| 21.19 | 452.43 |
| 10.77 | 477.81 |
| 28.68 | 431.66 |
| 29.87 | 431.8 |
| 22.99 | 446.67 |
| 24.66 | 445.26 |
| 32.63 | 425.72 |
| 31.38 | 430.58 |
| 23.87 | 439.86 |
| 25.6 | 441.11 |
| 27.62 | 434.72 |
| 30.1 | 434.01 |
| 12.19 | 475.64 |
| 13.11 | 460.44 |
| 28.29 | 436.4 |
| 13.45 | 461.03 |
| 10.98 | 479.08 |
| 26.48 | 435.76 |
| 13.07 | 460.14 |
| 25.56 | 442.2 |
| 22.68 | 447.69 |
| 28.86 | 431.15 |
| 22.7 | 445.0 |
| 27.89 | 431.59 |
| 13.78 | 467.22 |
| 28.14 | 445.33 |
| 11.8 | 470.57 |
| 10.71 | 473.77 |
| 24.54 | 447.67 |
| 11.54 | 474.29 |
| 29.47 | 437.14 |
| 29.24 | 432.56 |
| 14.51 | 459.14 |
| 22.91 | 446.19 |
| 27.02 | 428.1 |
| 13.49 | 468.46 |
| 30.24 | 435.02 |
| 23.19 | 445.52 |
| 17.73 | 462.69 |
| 18.62 | 455.75 |
| 12.85 | 463.74 |
| 32.33 | 439.79 |
| 25.09 | 443.26 |
| 29.45 | 432.04 |
| 16.91 | 465.86 |
| 14.09 | 465.6 |
| 10.73 | 469.43 |
| 23.2 | 440.75 |
| 8.21 | 481.32 |
| 9.3 | 479.87 |
| 16.97 | 458.59 |
| 23.69 | 438.62 |
| 25.13 | 445.59 |
| 9.86 | 481.87 |
| 11.33 | 475.01 |
| 26.95 | 436.54 |
| 15.0 | 456.63 |
| 20.76 | 451.69 |
| 14.29 | 463.04 |
| 19.74 | 446.1 |
| 26.68 | 438.67 |
| 14.24 | 466.88 |
| 21.98 | 444.6 |
| 22.75 | 440.26 |
| 8.34 | 483.92 |
| 11.8 | 475.19 |
| 8.81 | 479.24 |
| 30.05 | 434.92 |
| 16.01 | 454.16 |
| 21.75 | 447.58 |
| 13.94 | 467.9 |
| 29.25 | 426.29 |
| 22.33 | 447.02 |
| 16.43 | 455.85 |
| 11.5 | 476.46 |
| 23.53 | 437.48 |
| 21.86 | 452.77 |
| 6.17 | 491.54 |
| 30.19 | 438.41 |
| 11.67 | 476.1 |
| 15.34 | 464.58 |
| 11.5 | 467.74 |
| 25.53 | 442.12 |
| 21.27 | 453.34 |
| 28.37 | 425.29 |
| 28.39 | 449.63 |
| 13.78 | 462.88 |
| 14.6 | 464.67 |
| 5.1 | 489.96 |
| 7.0 | 482.38 |
| 26.3 | 437.95 |
| 30.56 | 429.2 |
| 21.09 | 453.34 |
| 28.21 | 442.47 |
| 15.84 | 462.6 |
| 10.03 | 478.79 |
| 20.37 | 456.11 |
| 21.19 | 450.33 |
| 33.73 | 434.83 |
| 29.87 | 433.43 |
| 19.62 | 456.02 |
| 9.93 | 485.23 |
| 9.43 | 473.57 |
| 14.24 | 469.94 |
| 12.97 | 452.07 |
| 7.6 | 475.32 |
| 8.39 | 480.69 |
| 25.41 | 444.01 |
| 18.43 | 465.17 |
| 10.31 | 480.61 |
| 11.29 | 476.04 |
| 22.61 | 441.76 |
| 29.34 | 428.24 |
| 18.87 | 444.77 |
| 13.21 | 463.1 |
| 11.3 | 470.5 |
| 29.23 | 431.0 |
| 27.76 | 430.68 |
| 29.26 | 436.42 |
| 25.72 | 452.33 |
| 23.43 | 440.16 |
| 25.6 | 435.75 |
| 22.3 | 449.74 |
| 27.91 | 430.73 |
| 30.35 | 432.75 |
| 21.78 | 446.79 |
| 7.19 | 486.35 |
| 20.88 | 453.18 |
| 24.19 | 458.31 |
| 9.98 | 480.26 |
| 23.47 | 448.65 |
| 26.35 | 458.41 |
| 29.89 | 435.39 |
| 19.29 | 450.21 |
| 17.48 | 459.59 |
| 25.21 | 445.84 |
| 23.3 | 441.08 |
| 15.42 | 467.33 |
| 21.44 | 444.19 |
| 29.45 | 432.96 |
| 29.69 | 438.09 |
| 15.52 | 467.9 |
| 11.47 | 475.72 |
| 9.77 | 477.51 |
| 22.6 | 435.13 |
| 8.24 | 477.9 |
| 17.01 | 457.26 |
| 19.64 | 467.53 |
| 10.61 | 465.15 |
| 12.04 | 474.28 |
| 29.19 | 444.49 |
| 21.75 | 452.84 |
| 23.66 | 435.38 |
| 27.05 | 433.57 |
| 29.63 | 435.27 |
| 18.2 | 468.49 |
| 32.22 | 433.07 |
| 26.88 | 430.63 |
| 29.05 | 440.74 |
| 8.9 | 474.49 |
| 18.93 | 449.74 |
| 27.49 | 436.73 |
| 23.1 | 434.58 |
| 11.22 | 473.93 |
| 31.97 | 435.99 |
| 13.32 | 466.83 |
| 31.68 | 427.22 |
| 23.69 | 444.07 |
| 13.83 | 469.57 |
| 18.32 | 459.89 |
| 11.05 | 479.59 |
| 22.03 | 440.92 |
| 10.23 | 480.87 |
| 23.92 | 441.9 |
| 29.38 | 430.2 |
| 17.35 | 465.16 |
| 9.81 | 471.32 |
| 4.97 | 485.43 |
| 5.15 | 495.35 |
| 21.54 | 449.12 |
| 7.94 | 480.53 |
| 18.77 | 457.07 |
| 21.69 | 443.67 |
| 10.07 | 477.52 |
| 13.83 | 472.95 |
| 10.45 | 472.54 |
| 11.56 | 469.17 |
| 23.64 | 435.21 |
| 10.48 | 477.78 |
| 13.09 | 475.89 |
| 10.67 | 483.9 |
| 12.57 | 476.2 |
| 14.45 | 462.16 |
| 14.22 | 471.05 |
| 6.97 | 484.71 |
| 20.61 | 446.34 |
| 14.67 | 469.02 |
| 29.06 | 432.12 |
| 14.38 | 467.28 |
| 32.51 | 429.66 |
| 11.79 | 469.49 |
| 8.65 | 485.87 |
| 9.75 | 481.95 |
| 9.11 | 479.03 |
| 23.39 | 434.5 |
| 14.3 | 464.9 |
| 17.49 | 452.71 |
| 31.1 | 429.74 |
| 19.77 | 457.09 |
| 28.61 | 446.77 |
| 13.52 | 460.76 |
| 13.52 | 471.95 |
| 17.57 | 453.29 |
| 28.18 | 441.61 |
| 14.29 | 464.73 |
| 18.12 | 464.68 |
| 31.27 | 430.59 |
| 26.24 | 438.01 |
| 7.44 | 479.08 |
| 29.78 | 436.39 |
| 23.37 | 447.07 |
| 10.62 | 479.91 |
| 5.84 | 489.05 |
| 14.51 | 463.17 |
| 11.31 | 471.26 |
| 11.25 | 480.49 |
| 9.18 | 473.78 |
| 19.82 | 455.5 |
| 24.77 | 446.27 |
| 9.66 | 482.2 |
| 21.96 | 452.48 |
| 18.59 | 464.48 |
| 24.75 | 438.1 |
| 24.37 | 445.6 |
| 29.6 | 442.43 |
| 25.32 | 436.67 |
| 16.15 | 466.56 |
| 15.74 | 457.29 |
| 5.97 | 487.03 |
| 15.84 | 464.93 |
| 14.84 | 466.0 |
| 12.25 | 469.52 |
| 27.38 | 428.88 |
| 8.76 | 474.3 |
| 15.54 | 461.06 |
| 18.71 | 465.57 |
| 13.06 | 467.67 |
| 12.72 | 466.99 |
| 19.83 | 463.72 |
| 27.23 | 443.78 |
| 24.27 | 445.23 |
| 11.8 | 464.43 |
| 6.76 | 484.36 |
| 25.99 | 442.16 |
| 16.3 | 464.11 |
| 16.5 | 462.48 |
| 10.59 | 477.49 |
| 26.05 | 437.04 |
| 19.5 | 457.09 |
| 22.21 | 450.6 |
| 17.86 | 465.78 |
| 29.96 | 427.1 |
| 19.08 | 459.81 |
| 23.59 | 447.36 |
| 3.38 | 488.92 |
| 26.39 | 433.36 |
| 8.99 | 483.35 |
| 10.91 | 469.53 |
| 13.08 | 476.96 |
| 23.95 | 440.75 |
| 15.64 | 462.55 |
| 18.78 | 448.04 |
| 20.65 | 455.24 |
| 4.96 | 494.75 |
| 23.51 | 444.58 |
| 5.99 | 484.82 |
| 23.65 | 442.9 |
| 5.17 | 485.46 |
| 26.38 | 457.81 |
| 6.02 | 481.92 |
| 23.2 | 443.23 |
| 8.57 | 474.29 |
| 30.72 | 430.46 |
| 21.52 | 455.71 |
| 22.93 | 438.34 |
| 5.71 | 485.83 |
| 18.62 | 452.82 |
| 27.88 | 435.04 |
| 22.32 | 451.21 |
| 14.55 | 465.81 |
| 17.83 | 458.42 |
| 9.68 | 470.22 |
| 19.41 | 449.24 |
| 13.22 | 471.43 |
| 12.24 | 473.26 |
| 19.21 | 452.82 |
| 29.74 | 432.69 |
| 23.28 | 444.13 |
| 8.02 | 467.21 |
| 22.47 | 445.98 |
| 27.51 | 436.91 |
| 17.51 | 455.01 |
| 23.22 | 437.11 |
| 11.73 | 477.06 |
| 21.19 | 441.71 |
| 5.48 | 495.76 |
| 24.26 | 445.63 |
| 12.32 | 464.72 |
| 31.26 | 438.03 |
| 32.09 | 434.78 |
| 24.98 | 444.67 |
| 27.48 | 452.24 |
| 21.04 | 450.92 |
| 27.75 | 436.53 |
| 22.79 | 435.53 |
| 24.22 | 440.01 |
| 27.06 | 443.1 |
| 29.25 | 427.49 |
| 26.86 | 436.25 |
| 29.64 | 440.74 |
| 19.92 | 443.54 |
| 18.5 | 459.42 |
| 23.71 | 439.66 |
| 14.39 | 464.15 |
| 19.3 | 459.1 |
| 24.65 | 455.68 |
| 13.5 | 469.08 |
| 9.82 | 478.02 |
| 18.4 | 456.8 |
| 28.12 | 441.13 |
| 17.15 | 463.88 |
| 30.69 | 430.45 |
| 28.82 | 449.18 |
| 21.3 | 447.89 |
| 30.58 | 431.59 |
| 21.17 | 447.5 |
| 9.87 | 475.58 |
| 22.18 | 453.24 |
| 24.39 | 446.4 |
| 10.73 | 476.81 |
| 9.38 | 474.1 |
| 20.27 | 450.71 |
| 24.82 | 433.62 |
| 16.55 | 465.14 |
| 20.73 | 445.18 |
| 9.51 | 474.12 |
| 8.63 | 483.91 |
| 6.48 | 486.68 |
| 14.95 | 464.98 |
| 5.76 | 481.4 |
| 10.94 | 479.2 |
| 15.87 | 463.86 |
| 12.42 | 472.3 |
| 29.12 | 446.51 |
| 29.12 | 437.71 |
| 19.08 | 458.94 |
| 31.06 | 437.91 |
| 5.72 | 490.76 |
| 26.52 | 439.66 |
| 13.84 | 463.27 |
| 13.03 | 473.99 |
| 25.94 | 433.38 |
| 16.64 | 459.01 |
| 14.13 | 471.44 |
| 13.65 | 471.91 |
| 14.5 | 465.15 |
| 19.8 | 446.66 |
| 25.2 | 438.15 |
| 20.66 | 447.14 |
| 12.07 | 472.32 |
| 25.64 | 441.68 |
| 23.33 | 440.04 |
| 29.41 | 444.82 |
| 16.6 | 457.26 |
| 27.53 | 428.83 |
| 20.62 | 449.07 |
| 26.02 | 435.21 |
| 12.75 | 471.03 |
| 12.87 | 465.56 |
| 25.77 | 442.83 |
| 14.84 | 460.3 |
| 7.41 | 474.25 |
| 8.87 | 477.97 |
| 9.69 | 472.16 |
| 16.17 | 456.08 |
| 26.24 | 452.41 |
| 13.78 | 463.71 |
| 26.3 | 433.72 |
| 17.37 | 456.4 |
| 23.6 | 448.43 |
| 8.3 | 481.6 |
| 18.86 | 457.07 |
| 22.12 | 451.0 |
| 28.41 | 440.28 |
| 29.42 | 437.47 |
| 18.61 | 443.57 |
| 27.57 | 426.6 |
| 12.83 | 470.87 |
| 9.64 | 478.37 |
| 19.13 | 453.92 |
| 15.92 | 470.22 |
| 24.64 | 434.54 |
| 27.62 | 442.89 |
| 8.9 | 479.03 |
| 9.55 | 476.06 |
| 10.57 | 473.88 |
| 19.8 | 451.75 |
| 25.63 | 439.2 |
| 24.7 | 439.7 |
| 15.26 | 463.6 |
| 20.06 | 447.47 |
| 19.84 | 447.92 |
| 11.49 | 471.08 |
| 23.74 | 437.55 |
| 22.62 | 448.27 |
| 29.53 | 431.69 |
| 21.32 | 449.09 |
| 20.3 | 448.79 |
| 16.97 | 460.21 |
| 12.07 | 479.28 |
| 7.46 | 483.11 |
| 19.2 | 450.75 |
| 28.64 | 437.97 |
| 13.56 | 459.76 |
| 17.4 | 457.75 |
| 14.08 | 469.33 |
| 27.11 | 433.28 |
| 20.92 | 444.64 |
| 16.18 | 463.1 |
| 15.57 | 460.91 |
| 10.37 | 479.35 |
| 19.6 | 449.23 |
| 9.22 | 474.51 |
| 27.76 | 435.02 |
| 28.68 | 435.45 |
| 20.95 | 452.38 |
| 9.06 | 480.41 |
| 9.21 | 478.96 |
| 13.65 | 468.87 |
| 31.79 | 434.01 |
| 14.32 | 466.36 |
| 26.28 | 435.28 |
| 7.69 | 486.46 |
| 14.44 | 468.19 |
| 9.19 | 468.37 |
| 13.35 | 474.19 |
| 23.04 | 440.32 |
| 4.83 | 485.32 |
| 17.29 | 464.27 |
| 8.73 | 479.25 |
| 26.21 | 430.4 |
| 23.72 | 447.49 |
| 29.27 | 438.23 |
| 10.4 | 492.09 |
| 12.19 | 475.36 |
| 20.4 | 452.56 |
| 34.3 | 427.84 |
| 27.56 | 433.95 |
| 30.9 | 435.27 |
| 14.85 | 454.62 |
| 16.42 | 472.17 |
| 16.45 | 452.42 |
| 10.14 | 472.17 |
| 9.53 | 481.83 |
| 17.01 | 458.78 |
| 23.94 | 447.5 |
| 15.95 | 463.4 |
| 11.15 | 473.57 |
| 25.56 | 433.72 |
| 27.16 | 431.85 |
| 26.71 | 433.47 |
| 29.56 | 432.84 |
| 31.19 | 436.6 |
| 6.86 | 490.23 |
| 12.36 | 477.16 |
| 32.82 | 441.06 |
| 25.3 | 440.86 |
| 8.71 | 477.94 |
| 13.34 | 474.47 |
| 14.2 | 470.67 |
| 23.74 | 447.31 |
| 16.9 | 466.8 |
| 28.54 | 430.91 |
| 30.15 | 434.75 |
| 14.33 | 469.52 |
| 25.57 | 438.9 |
| 30.55 | 429.56 |
| 28.04 | 432.92 |
| 26.39 | 442.87 |
| 15.3 | 466.59 |
| 6.03 | 479.61 |
| 13.49 | 471.08 |
| 27.67 | 433.37 |
| 24.19 | 443.92 |
| 24.44 | 443.5 |
| 29.86 | 439.89 |
| 30.2 | 434.66 |
| 7.99 | 487.57 |
| 9.93 | 464.64 |
| 11.03 | 470.92 |
| 22.34 | 444.39 |
| 25.33 | 442.48 |
| 18.87 | 449.61 |
| 25.97 | 435.02 |
| 16.58 | 458.67 |
| 14.35 | 461.74 |
| 25.06 | 438.31 |
| 13.85 | 462.38 |
| 16.09 | 460.56 |
| 26.34 | 439.22 |
| 23.01 | 444.64 |
| 26.39 | 430.34 |
| 31.32 | 430.46 |
| 16.64 | 456.79 |
| 13.42 | 468.82 |
| 20.06 | 448.51 |
| 14.8 | 470.77 |
| 12.59 | 465.74 |
| 26.7 | 430.21 |
| 19.78 | 449.23 |
| 15.17 | 461.89 |
| 21.71 | 445.72 |
| 19.09 | 466.13 |
| 19.76 | 448.71 |
| 14.68 | 469.25 |
| 21.3 | 450.56 |
| 16.73 | 464.46 |
| 12.26 | 471.13 |
| 14.77 | 461.52 |
| 18.26 | 451.09 |
| 27.1 | 431.51 |
| 14.72 | 469.8 |
| 26.3 | 442.28 |
| 16.48 | 458.67 |
| 17.99 | 462.4 |
| 20.34 | 453.54 |
| 25.53 | 444.38 |
| 31.59 | 440.52 |
| 30.8 | 433.62 |
| 10.75 | 481.96 |
| 19.3 | 452.75 |
| 4.71 | 481.28 |
| 23.1 | 439.03 |
| 32.63 | 435.75 |
| 26.63 | 436.03 |
| 24.35 | 445.6 |
| 15.11 | 462.65 |
| 29.1 | 438.66 |
| 21.24 | 447.32 |
| 6.16 | 484.55 |
| 7.36 | 476.8 |
| 10.44 | 480.34 |
| 26.76 | 440.63 |
| 16.79 | 459.48 |
| 10.76 | 490.78 |
| 6.07 | 483.56 |
| 27.33 | 429.38 |
| 27.15 | 440.27 |
| 22.35 | 445.34 |
| 21.82 | 447.43 |
| 21.11 | 439.91 |
| 19.95 | 459.27 |
| 7.45 | 478.89 |
| 15.36 | 466.7 |
| 15.65 | 463.5 |
| 25.31 | 436.21 |
| 25.88 | 443.94 |
| 24.6 | 439.63 |
| 22.58 | 460.95 |
| 19.69 | 448.69 |
| 25.85 | 444.63 |
| 10.06 | 473.51 |
| 18.59 | 462.56 |
| 18.27 | 451.76 |
| 8.85 | 491.81 |
| 30.04 | 429.52 |
| 26.06 | 437.9 |
| 14.8 | 467.54 |
| 23.93 | 449.97 |
| 23.72 | 436.62 |
| 11.44 | 477.68 |
| 20.28 | 447.26 |
| 27.9 | 439.76 |
| 24.74 | 437.49 |
| 14.8 | 455.14 |
| 8.22 | 485.5 |
| 27.56 | 444.1 |
| 32.07 | 432.33 |
| 9.53 | 471.23 |
| 13.61 | 463.89 |
| 22.2 | 445.54 |
| 21.36 | 446.09 |
| 23.25 | 445.12 |
| 23.5 | 443.31 |
| 8.46 | 484.16 |
| 8.19 | 477.76 |
| 30.67 | 430.28 |
| 32.48 | 446.48 |
| 8.99 | 481.03 |
| 13.77 | 466.07 |
| 19.05 | 447.47 |
| 21.19 | 455.93 |
| 10.12 | 479.62 |
| 24.93 | 455.06 |
| 8.47 | 475.06 |
| 24.52 | 438.89 |
| 28.55 | 432.7 |
| 20.58 | 452.6 |
| 18.31 | 451.75 |
| 27.18 | 430.66 |
| 4.43 | 491.9 |
| 26.02 | 439.82 |
| 15.75 | 460.73 |
| 22.99 | 449.7 |
| 25.52 | 439.42 |
| 27.04 | 439.84 |
| 6.42 | 485.86 |
| 17.04 | 458.1 |
| 10.79 | 479.92 |
| 20.41 | 458.29 |
| 7.36 | 489.45 |
| 28.08 | 434.0 |
| 24.74 | 431.24 |
| 28.32 | 439.5 |
| 16.71 | 467.46 |
| 30.7 | 429.27 |
| 18.42 | 452.1 |
| 10.62 | 472.41 |
| 22.18 | 442.14 |
| 22.38 | 441.0 |
| 13.94 | 463.07 |
| 21.24 | 445.71 |
| 6.76 | 483.16 |
| 26.73 | 440.45 |
| 7.24 | 481.83 |
| 10.84 | 467.6 |
| 19.32 | 450.88 |
| 29.0 | 425.5 |
| 23.38 | 451.87 |
| 31.17 | 428.94 |
| 26.17 | 439.86 |
| 30.9 | 433.44 |
| 24.92 | 438.23 |
| 32.77 | 436.95 |
| 14.37 | 470.19 |
| 8.36 | 484.66 |
| 31.45 | 430.81 |
| 31.6 | 433.37 |
| 17.9 | 453.02 |
| 20.35 | 453.5 |
| 16.21 | 463.09 |
| 19.36 | 464.56 |
| 21.04 | 452.12 |
| 14.05 | 470.9 |
| 23.48 | 450.89 |
| 21.91 | 445.04 |
| 24.42 | 444.72 |
| 14.26 | 460.38 |
| 21.38 | 446.8 |
| 15.71 | 465.05 |
| 5.78 | 484.13 |
| 6.77 | 488.27 |
| 23.84 | 447.09 |
| 21.17 | 452.02 |
| 19.94 | 455.55 |
| 8.73 | 480.99 |
| 16.39 | 467.68 |
| ExhaustVaccum | Power |
|---|---|
| 41.76 | 463.26 |
| 62.96 | 444.37 |
| 39.4 | 488.56 |
| 57.32 | 446.48 |
| 37.5 | 473.9 |
| 59.44 | 443.67 |
| 43.96 | 467.35 |
| 44.71 | 478.42 |
| 45.0 | 475.98 |
| 43.56 | 477.5 |
| 43.72 | 453.02 |
| 46.93 | 453.99 |
| 73.5 | 440.29 |
| 58.59 | 451.28 |
| 69.34 | 433.99 |
| 43.79 | 462.19 |
| 45.0 | 467.54 |
| 41.74 | 477.2 |
| 52.75 | 459.85 |
| 38.47 | 464.3 |
| 42.42 | 468.27 |
| 40.07 | 495.24 |
| 42.28 | 483.8 |
| 63.9 | 443.61 |
| 48.6 | 436.06 |
| 70.72 | 443.25 |
| 39.31 | 464.16 |
| 39.96 | 475.52 |
| 35.79 | 484.41 |
| 65.18 | 437.89 |
| 63.94 | 445.11 |
| 58.41 | 438.86 |
| 66.85 | 440.98 |
| 74.16 | 436.65 |
| 63.94 | 444.26 |
| 44.03 | 465.86 |
| 63.73 | 444.37 |
| 47.45 | 450.69 |
| 39.35 | 469.02 |
| 51.3 | 448.86 |
| 47.45 | 447.14 |
| 44.85 | 469.18 |
| 41.54 | 482.8 |
| 42.86 | 476.7 |
| 40.64 | 474.99 |
| 63.94 | 444.22 |
| 37.87 | 461.33 |
| 43.43 | 448.06 |
| 44.71 | 474.6 |
| 40.11 | 473.05 |
| 73.5 | 432.06 |
| 38.62 | 467.41 |
| 78.92 | 430.12 |
| 42.18 | 473.62 |
| 39.39 | 471.81 |
| 59.43 | 442.99 |
| 64.44 | 442.77 |
| 39.33 | 491.49 |
| 61.08 | 447.46 |
| 58.84 | 446.11 |
| 52.3 | 442.44 |
| 65.71 | 446.22 |
| 40.1 | 471.49 |
| 45.87 | 463.5 |
| 58.79 | 440.01 |
| 65.34 | 441.03 |
| 62.96 | 452.68 |
| 40.69 | 474.91 |
| 34.03 | 478.77 |
| 74.34 | 434.2 |
| 68.3 | 437.91 |
| 41.01 | 477.61 |
| 74.67 | 431.65 |
| 74.34 | 430.57 |
| 42.28 | 481.09 |
| 61.02 | 445.56 |
| 39.85 | 475.74 |
| 69.75 | 435.12 |
| 67.25 | 446.15 |
| 76.86 | 436.64 |
| 69.45 | 436.69 |
| 42.18 | 468.75 |
| 43.02 | 466.6 |
| 45.08 | 465.48 |
| 73.68 | 441.34 |
| 69.45 | 441.83 |
| 39.3 | 464.7 |
| 69.75 | 437.99 |
| 58.96 | 459.12 |
| 68.94 | 429.69 |
| 51.43 | 459.8 |
| 64.05 | 433.63 |
| 60.95 | 442.84 |
| 41.66 | 485.13 |
| 52.72 | 459.12 |
| 67.32 | 445.31 |
| 40.72 | 480.8 |
| 66.48 | 432.55 |
| 63.77 | 443.86 |
| 59.21 | 449.77 |
| 43.69 | 470.71 |
| 51.3 | 452.17 |
| 41.38 | 478.29 |
| 71.94 | 428.54 |
| 40.64 | 478.27 |
| 62.66 | 439.58 |
| 49.69 | 457.32 |
| 44.2 | 475.51 |
| 65.59 | 439.66 |
| 40.89 | 471.99 |
| 39.35 | 479.81 |
| 78.92 | 434.78 |
| 61.87 | 446.58 |
| 58.33 | 437.76 |
| 39.72 | 459.36 |
| 44.71 | 462.28 |
| 43.48 | 464.33 |
| 46.21 | 444.36 |
| 59.39 | 438.64 |
| 34.03 | 470.49 |
| 41.1 | 455.13 |
| 66.93 | 450.22 |
| 63.73 | 440.43 |
| 42.85 | 482.98 |
| 50.88 | 460.44 |
| 54.2 | 444.97 |
| 68.67 | 433.94 |
| 73.18 | 439.73 |
| 73.88 | 434.48 |
| 60.84 | 442.33 |
| 45.09 | 457.67 |
| 57.76 | 454.66 |
| 67.25 | 432.21 |
| 43.77 | 457.66 |
| 63.76 | 435.21 |
| 47.43 | 448.22 |
| 41.66 | 475.51 |
| 62.91 | 446.53 |
| 57.32 | 441.3 |
| 69.34 | 433.54 |
| 39.04 | 472.52 |
| 44.78 | 474.77 |
| 69.05 | 435.1 |
| 59.8 | 450.74 |
| 65.06 | 442.7 |
| 68.14 | 426.56 |
| 42.32 | 463.71 |
| 66.93 | 447.06 |
| 57.76 | 452.27 |
| 63.94 | 445.78 |
| 68.3 | 438.65 |
| 40.77 | 480.15 |
| 62.52 | 447.19 |
| 48.41 | 443.04 |
| 39.9 | 488.81 |
| 57.76 | 455.75 |
| 49.39 | 455.86 |
| 46.97 | 457.68 |
| 40.05 | 479.11 |
| 74.16 | 432.84 |
| 62.52 | 448.37 |
| 47.45 | 447.06 |
| 49.21 | 443.53 |
| 61.45 | 445.21 |
| 66.51 | 441.7 |
| 66.86 | 450.93 |
| 49.78 | 451.44 |
| 56.89 | 441.29 |
| 44.85 | 458.85 |
| 43.65 | 481.46 |
| 43.41 | 467.19 |
| 41.58 | 461.54 |
| 52.84 | 439.08 |
| 42.74 | 467.22 |
| 44.34 | 468.8 |
| 71.98 | 426.93 |
| 37.73 | 474.65 |
| 44.21 | 468.97 |
| 71.58 | 433.97 |
| 50.16 | 450.53 |
| 59.04 | 444.51 |
| 40.69 | 469.03 |
| 71.14 | 466.56 |
| 52.05 | 457.57 |
| 59.54 | 440.13 |
| 69.84 | 433.24 |
| 43.72 | 452.55 |
| 71.37 | 443.29 |
| 74.99 | 431.76 |
| 44.78 | 454.97 |
| 63.07 | 456.7 |
| 39.9 | 486.03 |
| 39.96 | 472.79 |
| 57.25 | 452.03 |
| 54.2 | 443.41 |
| 67.32 | 441.93 |
| 70.98 | 432.64 |
| 36.24 | 480.25 |
| 39.63 | 466.68 |
| 40.07 | 494.39 |
| 54.42 | 454.72 |
| 56.85 | 448.71 |
| 42.48 | 469.76 |
| 44.89 | 450.71 |
| 58.79 | 444.01 |
| 58.2 | 453.2 |
| 57.85 | 450.87 |
| 63.86 | 441.73 |
| 39.52 | 465.09 |
| 64.63 | 447.28 |
| 39.33 | 491.16 |
| 62.52 | 450.98 |
| 63.56 | 446.3 |
| 79.74 | 436.48 |
| 42.03 | 460.84 |
| 69.51 | 442.56 |
| 41.49 | 467.3 |
| 40.64 | 479.13 |
| 52.3 | 441.15 |
| 59.04 | 445.52 |
| 40.69 | 475.4 |
| 40.69 | 469.3 |
| 41.62 | 463.57 |
| 68.67 | 445.32 |
| 44.21 | 461.03 |
| 43.13 | 466.74 |
| 59.87 | 444.04 |
| 67.25 | 434.01 |
| 44.9 | 465.23 |
| 69.34 | 440.6 |
| 44.84 | 466.74 |
| 75.6 | 433.48 |
| 40.96 | 473.59 |
| 41.74 | 474.81 |
| 44.06 | 454.75 |
| 49.69 | 452.94 |
| 69.13 | 435.83 |
| 39.22 | 482.19 |
| 44.47 | 466.66 |
| 40.12 | 462.59 |
| 64.63 | 447.82 |
| 41.62 | 462.73 |
| 63.21 | 447.98 |
| 39.31 | 462.72 |
| 58.46 | 442.42 |
| 48.41 | 444.69 |
| 39.0 | 466.7 |
| 64.63 | 453.84 |
| 67.32 | 436.92 |
| 36.08 | 486.37 |
| 60.07 | 440.43 |
| 63.07 | 446.82 |
| 39.48 | 484.91 |
| 71.64 | 437.76 |
| 68.08 | 438.91 |
| 39.54 | 464.19 |
| 67.79 | 442.19 |
| 47.43 | 446.86 |
| 44.2 | 457.15 |
| 43.65 | 482.57 |
| 41.26 | 476.03 |
| 72.58 | 428.89 |
| 40.23 | 472.7 |
| 52.3 | 445.6 |
| 52.05 | 464.78 |
| 58.95 | 440.42 |
| 70.79 | 428.41 |
| 70.47 | 438.5 |
| 59.43 | 438.28 |
| 40.64 | 476.29 |
| 58.49 | 448.46 |
| 57.19 | 438.99 |
| 39.22 | 471.8 |
| 42.44 | 471.81 |
| 59.14 | 449.82 |
| 68.08 | 442.14 |
| 58.79 | 441.46 |
| 40.22 | 477.62 |
| 61.87 | 446.76 |
| 39.82 | 472.52 |
| 40.71 | 471.58 |
| 72.39 | 440.85 |
| 77.54 | 431.37 |
| 71.97 | 437.33 |
| 40.71 | 469.22 |
| 40.78 | 471.11 |
| 66.51 | 439.17 |
| 62.26 | 445.33 |
| 39.22 | 473.71 |
| 57.76 | 452.66 |
| 59.43 | 440.99 |
| 38.91 | 467.42 |
| 58.82 | 444.14 |
| 56.53 | 457.17 |
| 54.3 | 467.87 |
| 70.72 | 442.04 |
| 44.58 | 471.36 |
| 39.1 | 460.7 |
| 70.83 | 431.33 |
| 76.86 | 432.6 |
| 59.39 | 447.61 |
| 64.79 | 443.87 |
| 52.3 | 446.87 |
| 41.01 | 465.74 |
| 56.89 | 447.86 |
| 62.96 | 447.65 |
| 63.47 | 437.87 |
| 41.66 | 483.51 |
| 41.46 | 479.65 |
| 58.16 | 455.16 |
| 71.25 | 431.91 |
| 41.5 | 470.68 |
| 69.13 | 429.28 |
| 59.21 | 450.81 |
| 73.5 | 437.73 |
| 44.6 | 460.21 |
| 69.05 | 442.86 |
| 45.0 | 482.99 |
| 65.27 | 440.0 |
| 41.82 | 478.48 |
| 49.82 | 455.28 |
| 66.56 | 436.94 |
| 37.87 | 461.06 |
| 69.45 | 438.28 |
| 39.58 | 472.61 |
| 70.79 | 426.85 |
| 39.72 | 470.18 |
| 54.42 | 455.38 |
| 66.56 | 428.32 |
| 42.49 | 480.35 |
| 56.53 | 455.56 |
| 58.33 | 447.66 |
| 68.67 | 443.06 |
| 58.86 | 452.43 |
| 41.54 | 477.81 |
| 73.77 | 431.66 |
| 73.91 | 431.8 |
| 68.67 | 446.67 |
| 60.29 | 445.26 |
| 69.89 | 425.72 |
| 72.29 | 430.58 |
| 60.27 | 439.86 |
| 59.15 | 441.11 |
| 71.14 | 434.72 |
| 67.45 | 434.01 |
| 41.17 | 475.64 |
| 41.58 | 460.44 |
| 68.67 | 436.4 |
| 40.73 | 461.03 |
| 41.54 | 479.08 |
| 69.14 | 435.76 |
| 45.51 | 460.14 |
| 75.6 | 442.2 |
| 50.78 | 447.69 |
| 73.67 | 431.15 |
| 63.56 | 445.0 |
| 73.21 | 431.59 |
| 44.47 | 467.22 |
| 51.43 | 445.33 |
| 45.09 | 470.57 |
| 39.61 | 473.77 |
| 60.29 | 447.67 |
| 40.05 | 474.29 |
| 71.32 | 437.14 |
| 69.05 | 432.56 |
| 41.79 | 459.14 |
| 60.07 | 446.19 |
| 71.77 | 428.1 |
| 44.47 | 468.46 |
| 66.75 | 435.02 |
| 48.6 | 445.52 |
| 40.55 | 462.69 |
| 61.27 | 455.75 |
| 40.0 | 463.74 |
| 69.68 | 439.79 |
| 58.95 | 443.26 |
| 69.13 | 432.04 |
| 43.96 | 465.86 |
| 45.87 | 465.6 |
| 25.36 | 469.43 |
| 49.3 | 440.75 |
| 38.91 | 481.32 |
| 40.56 | 479.87 |
| 39.16 | 458.59 |
| 71.97 | 438.62 |
| 59.44 | 445.59 |
| 43.56 | 481.87 |
| 41.5 | 475.01 |
| 48.41 | 436.54 |
| 40.66 | 456.63 |
| 62.52 | 451.69 |
| 39.59 | 463.04 |
| 67.71 | 446.1 |
| 59.92 | 438.67 |
| 41.4 | 466.88 |
| 48.41 | 444.6 |
| 59.39 | 440.26 |
| 40.96 | 483.92 |
| 41.2 | 475.19 |
| 44.68 | 479.24 |
| 73.68 | 434.92 |
| 65.46 | 454.16 |
| 58.79 | 447.58 |
| 41.26 | 467.9 |
| 69.13 | 426.29 |
| 45.87 | 447.02 |
| 41.79 | 455.85 |
| 40.22 | 476.46 |
| 68.94 | 437.48 |
| 49.21 | 452.77 |
| 39.33 | 491.54 |
| 64.79 | 438.41 |
| 41.93 | 476.1 |
| 36.99 | 464.58 |
| 40.78 | 467.74 |
| 57.17 | 442.12 |
| 57.5 | 453.34 |
| 69.13 | 425.29 |
| 51.43 | 449.63 |
| 45.78 | 462.88 |
| 42.32 | 464.67 |
| 35.57 | 489.96 |
| 38.08 | 482.38 |
| 77.95 | 437.95 |
| 71.98 | 429.2 |
| 46.63 | 453.34 |
| 70.02 | 442.47 |
| 49.69 | 462.6 |
| 40.96 | 478.79 |
| 52.05 | 456.11 |
| 50.16 | 450.33 |
| 69.88 | 434.83 |
| 73.68 | 433.43 |
| 62.96 | 456.02 |
| 40.67 | 485.23 |
| 37.14 | 473.57 |
| 39.58 | 469.94 |
| 49.83 | 452.07 |
| 41.04 | 475.32 |
| 36.24 | 480.69 |
| 48.06 | 444.01 |
| 56.03 | 465.17 |
| 39.82 | 480.61 |
| 41.5 | 476.04 |
| 49.3 | 441.76 |
| 71.98 | 428.24 |
| 67.71 | 444.77 |
| 45.87 | 463.1 |
| 44.6 | 470.5 |
| 72.99 | 431.0 |
| 69.4 | 430.68 |
| 67.17 | 436.42 |
| 49.82 | 452.33 |
| 63.94 | 440.16 |
| 63.76 | 435.75 |
| 44.57 | 449.74 |
| 72.24 | 430.73 |
| 77.17 | 432.75 |
| 47.43 | 446.79 |
| 41.39 | 486.35 |
| 59.8 | 453.18 |
| 50.23 | 458.31 |
| 41.54 | 480.26 |
| 51.3 | 448.65 |
| 49.5 | 458.41 |
| 64.69 | 435.39 |
| 50.16 | 450.21 |
| 43.14 | 459.59 |
| 75.6 | 445.84 |
| 48.78 | 441.08 |
| 37.85 | 467.33 |
| 63.09 | 444.19 |
| 68.27 | 432.96 |
| 47.93 | 438.09 |
| 36.99 | 467.9 |
| 43.67 | 475.72 |
| 34.69 | 477.51 |
| 69.84 | 435.13 |
| 39.61 | 477.9 |
| 44.2 | 457.26 |
| 44.6 | 467.53 |
| 41.58 | 465.15 |
| 40.1 | 474.28 |
| 65.71 | 444.49 |
| 45.09 | 452.84 |
| 77.54 | 435.38 |
| 75.33 | 433.57 |
| 69.71 | 435.27 |
| 39.63 | 468.49 |
| 70.8 | 433.07 |
| 73.56 | 430.63 |
| 65.74 | 440.74 |
| 39.96 | 474.49 |
| 48.6 | 449.74 |
| 63.76 | 436.73 |
| 70.79 | 434.58 |
| 43.13 | 473.93 |
| 79.74 | 435.99 |
| 43.22 | 466.83 |
| 68.24 | 427.22 |
| 63.77 | 444.07 |
| 41.49 | 469.57 |
| 66.51 | 459.89 |
| 40.71 | 479.59 |
| 64.69 | 440.92 |
| 41.46 | 480.87 |
| 66.54 | 441.9 |
| 69.68 | 430.2 |
| 42.86 | 465.16 |
| 44.45 | 471.32 |
| 40.64 | 485.43 |
| 40.07 | 495.35 |
| 58.49 | 449.12 |
| 42.02 | 480.53 |
| 50.66 | 457.07 |
| 69.94 | 443.67 |
| 44.68 | 477.52 |
| 39.64 | 472.95 |
| 39.69 | 472.54 |
| 40.71 | 469.17 |
| 70.04 | 435.21 |
| 40.22 | 477.78 |
| 39.85 | 475.89 |
| 40.23 | 483.9 |
| 39.16 | 476.2 |
| 43.34 | 462.16 |
| 37.85 | 471.05 |
| 41.26 | 484.71 |
| 63.86 | 446.34 |
| 42.28 | 469.02 |
| 72.86 | 432.12 |
| 40.1 | 467.28 |
| 69.98 | 429.66 |
| 45.09 | 469.49 |
| 40.56 | 485.87 |
| 40.81 | 481.95 |
| 40.02 | 479.03 |
| 69.13 | 434.5 |
| 54.3 | 464.9 |
| 63.94 | 452.71 |
| 69.51 | 429.74 |
| 56.65 | 457.09 |
| 72.29 | 446.77 |
| 41.48 | 460.76 |
| 40.83 | 471.95 |
| 46.21 | 453.29 |
| 60.07 | 441.61 |
| 46.18 | 464.73 |
| 43.69 | 464.68 |
| 73.91 | 430.59 |
| 77.95 | 438.01 |
| 41.04 | 479.08 |
| 74.78 | 436.39 |
| 65.46 | 447.07 |
| 39.58 | 479.91 |
| 43.02 | 489.05 |
| 53.82 | 463.17 |
| 42.02 | 471.26 |
| 40.67 | 480.49 |
| 39.42 | 473.78 |
| 58.16 | 455.5 |
| 58.41 | 446.27 |
| 41.06 | 482.2 |
| 59.8 | 452.48 |
| 43.14 | 464.48 |
| 69.89 | 438.1 |
| 63.47 | 445.6 |
| 67.79 | 442.43 |
| 61.25 | 436.67 |
| 41.85 | 466.56 |
| 71.14 | 457.29 |
| 36.25 | 487.03 |
| 52.72 | 464.93 |
| 44.63 | 466.0 |
| 48.79 | 469.52 |
| 70.04 | 428.88 |
| 41.48 | 474.3 |
| 39.31 | 461.06 |
| 39.39 | 465.57 |
| 41.78 | 467.67 |
| 40.71 | 466.99 |
| 39.39 | 463.72 |
| 49.16 | 443.78 |
| 68.28 | 445.23 |
| 40.66 | 464.43 |
| 36.25 | 484.36 |
| 63.07 | 442.16 |
| 39.63 | 464.11 |
| 49.39 | 462.48 |
| 42.49 | 477.49 |
| 65.59 | 437.04 |
| 40.79 | 457.09 |
| 45.01 | 450.6 |
| 45.0 | 465.78 |
| 70.04 | 427.1 |
| 44.63 | 459.81 |
| 47.43 | 447.36 |
| 39.64 | 488.92 |
| 66.49 | 433.36 |
| 39.04 | 483.35 |
| 41.04 | 469.53 |
| 39.82 | 476.96 |
| 58.46 | 440.75 |
| 43.71 | 462.55 |
| 54.2 | 448.04 |
| 50.59 | 455.24 |
| 40.07 | 494.75 |
| 57.32 | 444.58 |
| 35.79 | 484.82 |
| 66.05 | 442.9 |
| 39.33 | 485.46 |
| 49.5 | 457.81 |
| 43.65 | 481.92 |
| 61.02 | 443.23 |
| 39.69 | 474.29 |
| 71.58 | 430.46 |
| 50.66 | 455.71 |
| 62.26 | 438.34 |
| 41.31 | 485.83 |
| 44.06 | 452.82 |
| 68.94 | 435.04 |
| 59.8 | 451.21 |
| 42.74 | 465.81 |
| 44.92 | 458.42 |
| 39.96 | 470.22 |
| 49.39 | 449.24 |
| 44.92 | 471.43 |
| 44.92 | 473.26 |
| 58.49 | 452.82 |
| 70.32 | 432.69 |
| 60.84 | 444.13 |
| 41.92 | 467.21 |
| 48.6 | 445.98 |
| 73.77 | 436.91 |
| 44.9 | 455.01 |
| 66.56 | 437.11 |
| 40.64 | 477.06 |
| 67.71 | 441.71 |
| 40.07 | 495.76 |
| 66.44 | 445.63 |
| 41.62 | 464.72 |
| 68.94 | 438.03 |
| 72.86 | 434.78 |
| 60.32 | 444.67 |
| 61.41 | 452.24 |
| 45.09 | 450.92 |
| 70.4 | 436.53 |
| 71.77 | 435.53 |
| 68.51 | 440.01 |
| 64.45 | 443.1 |
| 71.94 | 427.49 |
| 68.08 | 436.25 |
| 67.79 | 440.74 |
| 63.31 | 443.54 |
| 51.43 | 459.42 |
| 60.23 | 439.66 |
| 44.84 | 464.15 |
| 56.65 | 459.1 |
| 52.36 | 455.68 |
| 45.51 | 469.08 |
| 41.26 | 478.02 |
| 44.06 | 456.8 |
| 44.89 | 441.13 |
| 43.69 | 463.88 |
| 73.67 | 430.45 |
| 65.71 | 449.18 |
| 48.92 | 447.89 |
| 70.04 | 431.59 |
| 52.3 | 447.5 |
| 41.82 | 475.58 |
| 59.8 | 453.24 |
| 63.21 | 446.4 |
| 44.92 | 476.81 |
| 40.46 | 474.1 |
| 57.76 | 450.71 |
| 66.48 | 433.62 |
| 41.66 | 465.14 |
| 59.87 | 445.18 |
| 39.22 | 474.12 |
| 43.79 | 483.91 |
| 40.27 | 486.68 |
| 43.52 | 464.98 |
| 45.87 | 481.4 |
| 39.04 | 479.2 |
| 41.16 | 463.86 |
| 38.25 | 472.3 |
| 58.84 | 446.51 |
| 51.43 | 437.71 |
| 41.1 | 458.94 |
| 67.17 | 437.91 |
| 39.33 | 490.76 |
| 65.06 | 439.66 |
| 44.9 | 463.27 |
| 39.52 | 473.99 |
| 66.49 | 433.38 |
| 53.82 | 459.01 |
| 40.75 | 471.44 |
| 39.28 | 471.91 |
| 44.47 | 465.15 |
| 51.19 | 446.66 |
| 63.76 | 438.15 |
| 51.19 | 447.14 |
| 43.71 | 472.32 |
| 70.72 | 441.68 |
| 72.99 | 440.04 |
| 64.05 | 444.82 |
| 53.16 | 457.26 |
| 72.58 | 428.83 |
| 43.43 | 449.07 |
| 71.94 | 435.21 |
| 44.2 | 471.03 |
| 48.04 | 465.56 |
| 62.96 | 442.83 |
| 41.48 | 460.3 |
| 40.71 | 474.25 |
| 41.82 | 477.97 |
| 40.46 | 472.16 |
| 46.97 | 456.08 |
| 49.82 | 452.41 |
| 43.22 | 463.71 |
| 67.07 | 433.72 |
| 57.76 | 456.4 |
| 48.98 | 448.43 |
| 36.08 | 481.6 |
| 42.18 | 457.07 |
| 49.39 | 451.0 |
| 75.6 | 440.28 |
| 71.32 | 437.47 |
| 67.71 | 443.57 |
| 69.84 | 426.6 |
| 41.5 | 470.87 |
| 39.85 | 478.37 |
| 58.66 | 453.92 |
| 40.56 | 470.22 |
| 72.24 | 434.54 |
| 63.9 | 442.89 |
| 36.24 | 479.03 |
| 43.99 | 476.06 |
| 36.71 | 473.88 |
| 57.25 | 451.75 |
| 56.85 | 439.2 |
| 58.46 | 439.7 |
| 46.18 | 463.6 |
| 52.84 | 447.47 |
| 56.89 | 447.92 |
| 44.63 | 471.08 |
| 72.43 | 437.55 |
| 51.3 | 448.27 |
| 72.39 | 431.69 |
| 48.14 | 449.09 |
| 58.46 | 448.79 |
| 44.92 | 460.21 |
| 41.17 | 479.28 |
| 41.82 | 483.11 |
| 54.2 | 450.75 |
| 66.54 | 437.97 |
| 41.48 | 459.76 |
| 44.9 | 457.75 |
| 40.1 | 469.33 |
| 69.75 | 433.28 |
| 70.02 | 444.64 |
| 44.9 | 463.1 |
| 44.68 | 460.91 |
| 39.04 | 479.35 |
| 59.21 | 449.23 |
| 40.92 | 474.51 |
| 72.99 | 435.02 |
| 70.72 | 435.45 |
| 48.14 | 452.38 |
| 39.3 | 480.41 |
| 39.72 | 478.96 |
| 42.74 | 468.87 |
| 76.2 | 434.01 |
| 44.6 | 466.36 |
| 75.23 | 435.28 |
| 43.02 | 486.46 |
| 40.1 | 468.19 |
| 41.01 | 468.37 |
| 41.39 | 474.19 |
| 74.22 | 440.32 |
| 38.44 | 485.32 |
| 42.86 | 464.27 |
| 36.18 | 479.25 |
| 70.32 | 430.4 |
| 58.62 | 447.49 |
| 64.69 | 438.23 |
| 40.43 | 492.09 |
| 40.75 | 475.36 |
| 54.9 | 452.56 |
| 74.67 | 427.84 |
| 68.08 | 433.95 |
| 70.8 | 435.27 |
| 58.59 | 454.62 |
| 40.56 | 472.17 |
| 63.31 | 452.42 |
| 42.02 | 472.17 |
| 41.44 | 481.83 |
| 49.15 | 458.78 |
| 62.08 | 447.5 |
| 49.25 | 463.4 |
| 41.26 | 473.57 |
| 70.32 | 433.72 |
| 66.44 | 431.85 |
| 77.95 | 433.47 |
| 74.22 | 432.84 |
| 70.94 | 436.6 |
| 41.17 | 490.23 |
| 41.74 | 477.16 |
| 68.31 | 441.06 |
| 70.98 | 440.86 |
| 41.82 | 477.94 |
| 40.8 | 474.47 |
| 43.02 | 470.67 |
| 65.34 | 447.31 |
| 44.88 | 466.8 |
| 71.94 | 430.91 |
| 69.88 | 434.75 |
| 42.86 | 469.52 |
| 59.43 | 438.9 |
| 70.04 | 429.56 |
| 74.33 | 432.92 |
| 49.16 | 442.87 |
| 41.76 | 466.59 |
| 41.14 | 479.61 |
| 44.63 | 471.08 |
| 59.14 | 433.37 |
| 65.48 | 443.92 |
| 59.14 | 443.5 |
| 64.79 | 439.89 |
| 69.59 | 434.66 |
| 41.38 | 487.57 |
| 41.62 | 464.64 |
| 42.32 | 470.92 |
| 63.73 | 444.39 |
| 48.6 | 442.48 |
| 52.08 | 449.61 |
| 69.34 | 435.02 |
| 43.99 | 458.67 |
| 46.18 | 461.74 |
| 62.39 | 438.31 |
| 48.92 | 462.38 |
| 44.2 | 460.56 |
| 59.21 | 439.22 |
| 58.79 | 444.64 |
| 71.25 | 430.34 |
| 71.29 | 430.46 |
| 45.87 | 456.79 |
| 41.23 | 468.82 |
| 44.9 | 448.51 |
| 44.71 | 470.77 |
| 41.14 | 465.74 |
| 66.56 | 430.21 |
| 50.32 | 449.23 |
| 49.15 | 461.89 |
| 61.45 | 445.72 |
| 39.39 | 466.13 |
| 51.19 | 448.71 |
| 41.23 | 469.25 |
| 66.86 | 450.56 |
| 39.64 | 464.46 |
| 41.5 | 471.13 |
| 48.06 | 461.52 |
| 59.15 | 451.09 |
| 79.74 | 431.51 |
| 40.83 | 469.8 |
| 51.43 | 442.28 |
| 48.92 | 458.67 |
| 43.79 | 462.4 |
| 59.8 | 453.54 |
| 62.96 | 444.38 |
| 58.9 | 440.52 |
| 69.14 | 433.62 |
| 45.0 | 481.96 |
| 44.9 | 452.75 |
| 39.42 | 481.28 |
| 66.05 | 439.03 |
| 73.88 | 435.75 |
| 74.16 | 436.03 |
| 58.49 | 445.6 |
| 56.03 | 462.65 |
| 50.05 | 438.66 |
| 50.32 | 447.32 |
| 39.48 | 484.55 |
| 41.01 | 476.8 |
| 39.04 | 480.34 |
| 48.41 | 440.63 |
| 44.6 | 459.48 |
| 40.43 | 490.78 |
| 38.91 | 483.56 |
| 73.18 | 429.38 |
| 59.21 | 440.27 |
| 51.43 | 445.34 |
| 65.27 | 447.43 |
| 69.94 | 439.91 |
| 50.59 | 459.27 |
| 39.61 | 478.89 |
| 41.66 | 466.7 |
| 43.5 | 463.5 |
| 74.33 | 436.21 |
| 63.47 | 443.94 |
| 63.94 | 439.63 |
| 41.54 | 460.95 |
| 59.14 | 448.69 |
| 75.08 | 444.63 |
| 37.83 | 473.51 |
| 39.54 | 462.56 |
| 50.16 | 451.76 |
| 40.43 | 491.81 |
| 68.08 | 429.52 |
| 49.02 | 437.9 |
| 38.73 | 467.54 |
| 64.45 | 449.97 |
| 66.48 | 436.62 |
| 40.55 | 477.68 |
| 63.86 | 447.26 |
| 63.13 | 439.76 |
| 59.39 | 437.49 |
| 58.2 | 455.14 |
| 41.03 | 485.5 |
| 66.93 | 444.1 |
| 70.94 | 432.33 |
| 44.03 | 471.23 |
| 42.34 | 463.89 |
| 51.19 | 445.54 |
| 59.54 | 446.09 |
| 63.86 | 445.12 |
| 59.21 | 443.31 |
| 39.66 | 484.16 |
| 40.69 | 477.76 |
| 71.29 | 430.28 |
| 62.04 | 446.48 |
| 36.66 | 481.03 |
| 47.83 | 466.07 |
| 67.32 | 447.47 |
| 55.5 | 455.93 |
| 40.0 | 479.62 |
| 47.01 | 455.06 |
| 40.46 | 475.06 |
| 56.85 | 438.89 |
| 69.84 | 432.7 |
| 50.9 | 452.6 |
| 46.21 | 451.75 |
| 71.06 | 430.66 |
| 38.91 | 491.9 |
| 74.78 | 439.82 |
| 39.0 | 460.73 |
| 60.95 | 449.7 |
| 59.15 | 439.42 |
| 65.06 | 439.84 |
| 35.57 | 485.86 |
| 40.12 | 458.1 |
| 39.82 | 479.92 |
| 56.03 | 458.29 |
| 40.07 | 489.45 |
| 73.42 | 434.0 |
| 69.13 | 431.24 |
| 47.93 | 439.5 |
| 40.56 | 467.46 |
| 71.58 | 429.27 |
| 58.95 | 452.1 |
| 42.02 | 472.41 |
| 69.05 | 442.14 |
| 49.3 | 441.0 |
| 41.58 | 463.07 |
| 60.84 | 445.71 |
| 39.81 | 483.16 |
| 68.84 | 440.45 |
| 38.06 | 481.83 |
| 40.62 | 467.6 |
| 52.84 | 450.88 |
| 69.13 | 425.5 |
| 54.42 | 451.87 |
| 69.51 | 428.94 |
| 48.6 | 439.86 |
| 73.42 | 433.44 |
| 73.68 | 438.23 |
| 71.32 | 436.95 |
| 40.56 | 470.19 |
| 40.22 | 484.66 |
| 68.27 | 430.81 |
| 73.17 | 433.37 |
| 48.98 | 453.02 |
| 50.9 | 453.5 |
| 41.23 | 463.09 |
| 44.6 | 464.56 |
| 65.46 | 452.12 |
| 40.69 | 470.9 |
| 64.15 | 450.89 |
| 63.76 | 445.04 |
| 63.07 | 444.72 |
| 40.92 | 460.38 |
| 58.33 | 446.8 |
| 44.06 | 465.05 |
| 40.62 | 484.13 |
| 39.81 | 488.27 |
| 49.21 | 447.09 |
| 58.16 | 452.02 |
| 58.96 | 455.55 |
| 41.92 | 480.99 |
| 41.67 | 467.68 |
| Pressure | Power |
|---|---|
| 1024.07 | 463.26 |
| 1020.04 | 444.37 |
| 1012.16 | 488.56 |
| 1010.24 | 446.48 |
| 1009.23 | 473.9 |
| 1012.23 | 443.67 |
| 1014.02 | 467.35 |
| 1019.12 | 478.42 |
| 1021.78 | 475.98 |
| 1015.14 | 477.5 |
| 1008.64 | 453.02 |
| 1014.66 | 453.99 |
| 1011.31 | 440.29 |
| 1012.77 | 451.28 |
| 1009.48 | 433.99 |
| 1015.76 | 462.19 |
| 1022.86 | 467.54 |
| 1022.6 | 477.2 |
| 1023.97 | 459.85 |
| 1015.15 | 464.3 |
| 1009.09 | 468.27 |
| 1019.16 | 495.24 |
| 1008.52 | 483.8 |
| 1014.3 | 443.61 |
| 1003.18 | 436.06 |
| 1009.97 | 443.25 |
| 1011.11 | 464.16 |
| 1023.57 | 475.52 |
| 1012.14 | 484.41 |
| 1012.69 | 437.89 |
| 1019.02 | 445.11 |
| 1013.64 | 438.86 |
| 1011.11 | 440.98 |
| 1010.08 | 436.65 |
| 1018.76 | 444.26 |
| 1007.29 | 465.86 |
| 1011.4 | 444.37 |
| 1010.08 | 450.69 |
| 1014.69 | 469.02 |
| 1012.04 | 448.86 |
| 1007.62 | 447.14 |
| 1017.24 | 469.18 |
| 1018.33 | 482.8 |
| 1014.12 | 476.7 |
| 1020.63 | 474.99 |
| 1019.28 | 444.22 |
| 1020.24 | 461.33 |
| 1010.96 | 448.06 |
| 1014.51 | 474.6 |
| 1029.14 | 473.05 |
| 1010.58 | 432.06 |
| 1018.71 | 467.41 |
| 1011.6 | 430.12 |
| 1014.82 | 473.62 |
| 1012.94 | 471.81 |
| 1010.23 | 442.99 |
| 1014.65 | 442.77 |
| 1010.18 | 491.49 |
| 1013.68 | 447.46 |
| 1002.25 | 446.11 |
| 1007.03 | 442.44 |
| 1013.61 | 446.22 |
| 1016.67 | 471.49 |
| 1008.89 | 463.5 |
| 1016.02 | 440.01 |
| 1014.56 | 441.03 |
| 1019.49 | 452.68 |
| 1020.43 | 474.91 |
| 1018.71 | 478.77 |
| 998.14 | 434.2 |
| 1017.83 | 437.91 |
| 1024.61 | 477.61 |
| 1016.65 | 431.65 |
| 998.58 | 430.57 |
| 1008.82 | 481.09 |
| 1009.56 | 445.56 |
| 1012.71 | 475.74 |
| 1010.36 | 435.12 |
| 1017.39 | 446.15 |
| 1001.31 | 436.64 |
| 1013.89 | 436.69 |
| 1016.53 | 468.75 |
| 1012.18 | 466.6 |
| 1024.42 | 465.48 |
| 1014.93 | 441.34 |
| 1012.53 | 441.83 |
| 1019.0 | 464.7 |
| 1009.6 | 437.99 |
| 1015.55 | 459.12 |
| 1006.56 | 429.69 |
| 1010.57 | 459.8 |
| 1009.81 | 433.63 |
| 1014.62 | 442.84 |
| 1014.49 | 485.13 |
| 1025.09 | 459.12 |
| 1012.05 | 445.31 |
| 1022.7 | 480.8 |
| 1005.23 | 432.55 |
| 1013.42 | 443.86 |
| 1018.3 | 449.77 |
| 1017.19 | 470.71 |
| 1011.93 | 452.17 |
| 1021.6 | 478.29 |
| 1006.96 | 428.54 |
| 1020.66 | 478.27 |
| 1007.63 | 439.58 |
| 1005.53 | 457.32 |
| 1018.79 | 475.51 |
| 1010.85 | 439.66 |
| 1011.03 | 471.99 |
| 1015.1 | 479.81 |
| 1010.83 | 434.78 |
| 1011.18 | 446.58 |
| 1013.92 | 437.76 |
| 1001.24 | 459.36 |
| 1016.99 | 462.28 |
| 1016.08 | 464.33 |
| 1010.71 | 444.36 |
| 1014.32 | 438.64 |
| 1018.69 | 470.49 |
| 1001.86 | 455.13 |
| 1017.06 | 450.22 |
| 1009.34 | 440.43 |
| 1014.02 | 482.98 |
| 1014.19 | 460.44 |
| 1012.81 | 444.97 |
| 1005.2 | 433.94 |
| 1012.28 | 439.73 |
| 1005.89 | 434.48 |
| 1017.93 | 442.33 |
| 1014.26 | 457.67 |
| 1018.8 | 454.66 |
| 1017.71 | 432.21 |
| 1012.25 | 457.66 |
| 1010.27 | 435.21 |
| 1007.64 | 448.22 |
| 1013.79 | 475.51 |
| 1013.24 | 446.53 |
| 1011.7 | 441.3 |
| 1007.67 | 433.54 |
| 1020.45 | 472.52 |
| 1012.59 | 474.77 |
| 1001.62 | 435.1 |
| 1016.92 | 450.74 |
| 1014.32 | 442.7 |
| 1003.34 | 426.56 |
| 1016.0 | 463.71 |
| 1016.85 | 447.06 |
| 1018.02 | 452.27 |
| 1018.49 | 445.78 |
| 1017.93 | 438.65 |
| 1011.55 | 480.15 |
| 1016.46 | 447.19 |
| 1008.47 | 443.04 |
| 1008.06 | 488.81 |
| 1016.26 | 455.75 |
| 1018.83 | 455.86 |
| 1013.94 | 457.68 |
| 1014.95 | 479.11 |
| 1007.44 | 432.84 |
| 1016.18 | 448.37 |
| 1007.56 | 447.06 |
| 1014.1 | 443.53 |
| 1011.13 | 445.21 |
| 1015.53 | 441.7 |
| 1013.05 | 450.93 |
| 1002.95 | 451.44 |
| 1012.32 | 441.29 |
| 1014.48 | 458.85 |
| 1018.24 | 481.46 |
| 1016.93 | 467.19 |
| 1020.5 | 461.54 |
| 1006.28 | 439.08 |
| 1028.79 | 467.22 |
| 1019.49 | 468.8 |
| 1004.66 | 426.93 |
| 1024.36 | 474.65 |
| 1022.93 | 468.97 |
| 1010.18 | 433.97 |
| 1009.52 | 450.53 |
| 1011.98 | 444.51 |
| 1015.29 | 469.03 |
| 1019.36 | 466.56 |
| 1012.15 | 457.57 |
| 1006.24 | 440.13 |
| 1003.57 | 433.24 |
| 1008.64 | 452.55 |
| 1002.04 | 443.29 |
| 1005.47 | 431.76 |
| 1007.81 | 454.97 |
| 1012.42 | 456.7 |
| 1007.75 | 486.03 |
| 1011.37 | 472.79 |
| 1010.12 | 452.03 |
| 1012.26 | 443.41 |
| 1014.49 | 441.93 |
| 1007.51 | 432.64 |
| 1013.15 | 480.25 |
| 1004.47 | 466.68 |
| 1020.19 | 494.39 |
| 1012.46 | 454.72 |
| 1012.28 | 448.71 |
| 1013.43 | 469.76 |
| 1009.64 | 450.71 |
| 1009.8 | 444.01 |
| 1017.46 | 453.2 |
| 1012.39 | 450.87 |
| 1019.67 | 441.73 |
| 1018.41 | 465.09 |
| 1020.59 | 447.28 |
| 1011.54 | 491.16 |
| 1017.99 | 450.98 |
| 1013.75 | 446.3 |
| 1008.37 | 436.48 |
| 1017.41 | 460.84 |
| 1013.43 | 442.56 |
| 1020.19 | 467.3 |
| 1021.47 | 479.13 |
| 1008.72 | 441.15 |
| 1011.78 | 445.52 |
| 1015.62 | 475.4 |
| 1014.91 | 469.3 |
| 1017.17 | 463.57 |
| 1006.71 | 445.32 |
| 1020.36 | 461.03 |
| 1014.99 | 466.74 |
| 1018.47 | 444.04 |
| 1017.6 | 434.01 |
| 1020.5 | 465.23 |
| 1009.63 | 440.6 |
| 1023.66 | 466.74 |
| 1017.43 | 433.48 |
| 1023.36 | 473.59 |
| 1020.75 | 474.81 |
| 1017.58 | 454.75 |
| 1009.6 | 452.94 |
| 1009.94 | 435.83 |
| 1014.53 | 482.19 |
| 1030.46 | 466.66 |
| 1013.03 | 462.59 |
| 1020.69 | 447.82 |
| 1014.55 | 462.73 |
| 1012.06 | 447.98 |
| 1009.15 | 462.72 |
| 1016.82 | 442.42 |
| 1008.64 | 444.69 |
| 1016.73 | 466.7 |
| 1020.38 | 453.84 |
| 1013.83 | 436.92 |
| 1021.82 | 486.37 |
| 1015.42 | 440.43 |
| 1010.94 | 446.82 |
| 1005.11 | 484.91 |
| 1008.27 | 437.76 |
| 1013.27 | 438.91 |
| 1007.97 | 464.19 |
| 1009.89 | 442.19 |
| 1008.38 | 446.86 |
| 1018.89 | 457.15 |
| 1020.14 | 482.57 |
| 1007.44 | 476.03 |
| 1008.69 | 428.89 |
| 1018.07 | 472.7 |
| 1009.04 | 445.6 |
| 1014.63 | 464.78 |
| 1017.02 | 440.42 |
| 1003.96 | 428.41 |
| 1010.65 | 438.5 |
| 1007.84 | 438.28 |
| 1022.35 | 476.29 |
| 1011.5 | 448.46 |
| 1008.62 | 438.99 |
| 1017.9 | 471.8 |
| 1012.74 | 471.81 |
| 1016.12 | 449.82 |
| 1013.13 | 442.14 |
| 1009.74 | 441.46 |
| 1011.37 | 477.62 |
| 1011.45 | 446.76 |
| 1012.46 | 472.52 |
| 1021.27 | 471.58 |
| 1001.15 | 440.85 |
| 1011.33 | 431.37 |
| 1008.64 | 437.33 |
| 1015.68 | 469.22 |
| 1023.51 | 471.11 |
| 1010.98 | 439.17 |
| 1012.11 | 445.33 |
| 1018.62 | 473.71 |
| 1016.28 | 452.66 |
| 1007.12 | 440.99 |
| 1014.48 | 467.42 |
| 1010.02 | 444.14 |
| 1020.57 | 457.17 |
| 1015.92 | 467.87 |
| 1009.78 | 442.04 |
| 1019.52 | 471.36 |
| 1009.81 | 460.7 |
| 1010.35 | 431.33 |
| 998.59 | 432.6 |
| 1014.07 | 447.61 |
| 1016.27 | 443.87 |
| 1009.2 | 446.87 |
| 1020.57 | 465.74 |
| 1014.02 | 447.86 |
| 1020.16 | 447.65 |
| 1011.78 | 437.87 |
| 1016.87 | 483.51 |
| 1018.21 | 479.65 |
| 1016.88 | 455.16 |
| 1002.49 | 431.91 |
| 1014.13 | 470.68 |
| 1009.29 | 429.28 |
| 1018.32 | 450.81 |
| 1011.49 | 437.73 |
| 1015.16 | 460.21 |
| 1001.6 | 442.86 |
| 1021.51 | 482.99 |
| 1013.27 | 440.0 |
| 1033.25 | 478.48 |
| 1015.01 | 455.28 |
| 1002.07 | 436.94 |
| 1022.36 | 461.06 |
| 1013.97 | 438.28 |
| 1011.81 | 472.61 |
| 1003.7 | 426.85 |
| 1017.76 | 470.18 |
| 1012.31 | 455.38 |
| 1006.44 | 428.32 |
| 1010.57 | 480.35 |
| 1020.2 | 455.56 |
| 1013.14 | 447.66 |
| 1006.74 | 443.06 |
| 1014.19 | 452.43 |
| 1019.94 | 477.81 |
| 1004.72 | 431.66 |
| 1004.53 | 431.8 |
| 1006.65 | 446.67 |
| 1018.0 | 445.26 |
| 1013.85 | 425.72 |
| 1008.73 | 430.58 |
| 1018.94 | 439.86 |
| 1013.31 | 441.11 |
| 1011.6 | 434.72 |
| 1014.23 | 434.01 |
| 1019.43 | 475.64 |
| 1020.43 | 460.44 |
| 1005.46 | 436.4 |
| 1018.7 | 461.03 |
| 1019.94 | 479.08 |
| 1009.31 | 435.76 |
| 1015.22 | 460.14 |
| 1017.37 | 442.2 |
| 1008.83 | 447.69 |
| 1006.65 | 431.15 |
| 1014.32 | 445.0 |
| 1001.32 | 431.59 |
| 1027.94 | 467.22 |
| 1012.16 | 445.33 |
| 1013.21 | 470.57 |
| 1018.72 | 473.77 |
| 1017.42 | 447.67 |
| 1014.78 | 474.29 |
| 1008.07 | 437.14 |
| 1003.12 | 432.56 |
| 1009.72 | 459.14 |
| 1016.03 | 446.19 |
| 1006.38 | 428.1 |
| 1030.18 | 468.46 |
| 1017.95 | 435.02 |
| 1002.38 | 445.52 |
| 1003.36 | 462.69 |
| 1019.26 | 455.75 |
| 1015.89 | 463.74 |
| 1011.95 | 439.79 |
| 1016.99 | 443.26 |
| 1009.3 | 432.04 |
| 1013.32 | 465.86 |
| 1009.05 | 465.6 |
| 1009.35 | 469.43 |
| 1003.4 | 440.75 |
| 1015.82 | 481.32 |
| 1022.64 | 479.87 |
| 1005.7 | 458.59 |
| 1009.62 | 438.62 |
| 1012.38 | 445.59 |
| 1015.13 | 481.87 |
| 1013.58 | 475.01 |
| 1008.53 | 436.54 |
| 1016.28 | 456.63 |
| 1015.63 | 451.69 |
| 1010.93 | 463.04 |
| 1007.68 | 446.1 |
| 1009.94 | 438.67 |
| 1019.7 | 466.88 |
| 1008.42 | 444.6 |
| 1015.4 | 440.26 |
| 1023.28 | 483.92 |
| 1017.18 | 475.19 |
| 1023.06 | 479.24 |
| 1014.95 | 434.92 |
| 1014.0 | 454.16 |
| 1012.42 | 447.58 |
| 1021.67 | 467.9 |
| 1010.27 | 426.29 |
| 1007.8 | 447.02 |
| 1005.47 | 455.85 |
| 1010.31 | 476.46 |
| 1007.53 | 437.48 |
| 1014.61 | 452.77 |
| 1012.57 | 491.54 |
| 1017.22 | 438.41 |
| 1019.81 | 476.1 |
| 1007.87 | 464.58 |
| 1023.91 | 467.74 |
| 1010.0 | 442.12 |
| 1014.53 | 453.34 |
| 1010.44 | 425.29 |
| 1011.74 | 449.63 |
| 1025.27 | 462.88 |
| 1015.71 | 464.67 |
| 1027.17 | 489.96 |
| 1020.27 | 482.38 |
| 1009.45 | 437.95 |
| 1004.74 | 429.2 |
| 1013.03 | 453.34 |
| 1010.58 | 442.47 |
| 1015.14 | 462.6 |
| 1024.57 | 478.79 |
| 1012.34 | 456.11 |
| 1005.81 | 450.33 |
| 1007.21 | 434.83 |
| 1015.1 | 433.43 |
| 1020.76 | 456.02 |
| 1018.08 | 485.23 |
| 1013.03 | 473.57 |
| 1011.17 | 469.94 |
| 1008.69 | 452.07 |
| 1021.82 | 475.32 |
| 1013.39 | 480.69 |
| 1013.12 | 444.01 |
| 1020.41 | 465.17 |
| 1012.87 | 480.61 |
| 1013.39 | 476.04 |
| 1003.51 | 441.76 |
| 1005.19 | 428.24 |
| 1004.0 | 444.77 |
| 1008.58 | 463.1 |
| 1018.19 | 470.5 |
| 1007.04 | 431.0 |
| 1004.27 | 430.68 |
| 1006.6 | 436.42 |
| 1016.19 | 452.33 |
| 1010.64 | 440.16 |
| 1010.18 | 435.75 |
| 1008.48 | 449.74 |
| 1010.74 | 430.73 |
| 1009.55 | 432.75 |
| 1007.88 | 446.79 |
| 1018.12 | 486.35 |
| 1015.66 | 453.18 |
| 1015.73 | 458.31 |
| 1019.7 | 480.26 |
| 1011.89 | 448.65 |
| 1012.67 | 458.41 |
| 1006.37 | 435.39 |
| 1010.49 | 450.21 |
| 1018.68 | 459.59 |
| 1017.19 | 445.84 |
| 1018.17 | 441.08 |
| 1009.89 | 467.33 |
| 1016.56 | 444.19 |
| 1007.96 | 432.96 |
| 1002.85 | 438.09 |
| 1006.86 | 467.9 |
| 1012.68 | 475.72 |
| 1027.72 | 477.51 |
| 1006.37 | 435.13 |
| 1017.99 | 477.9 |
| 1019.18 | 457.26 |
| 1015.88 | 467.53 |
| 1021.08 | 465.15 |
| 1014.42 | 474.28 |
| 1013.85 | 444.49 |
| 1014.15 | 452.84 |
| 1008.5 | 435.38 |
| 1003.88 | 433.57 |
| 1009.04 | 435.27 |
| 1005.35 | 468.49 |
| 1009.9 | 433.07 |
| 1004.85 | 430.63 |
| 1013.29 | 440.74 |
| 1026.31 | 474.49 |
| 1005.72 | 449.74 |
| 1010.09 | 436.73 |
| 1006.53 | 434.58 |
| 1017.24 | 473.93 |
| 1007.03 | 435.99 |
| 1009.45 | 466.83 |
| 1005.29 | 427.22 |
| 1013.39 | 444.07 |
| 1020.11 | 469.57 |
| 1015.18 | 459.89 |
| 1024.91 | 479.59 |
| 1007.21 | 440.92 |
| 1020.45 | 480.87 |
| 1009.93 | 441.9 |
| 1011.35 | 430.2 |
| 1014.62 | 465.16 |
| 1021.19 | 471.32 |
| 1020.91 | 485.43 |
| 1012.27 | 495.35 |
| 1010.85 | 449.12 |
| 1006.22 | 480.53 |
| 1014.89 | 457.07 |
| 1010.7 | 443.67 |
| 1023.44 | 477.52 |
| 1012.52 | 472.95 |
| 1003.92 | 472.54 |
| 1015.85 | 469.17 |
| 1011.09 | 435.21 |
| 1004.81 | 477.78 |
| 1012.86 | 475.89 |
| 1017.75 | 483.9 |
| 1016.53 | 476.2 |
| 1015.47 | 462.16 |
| 1011.24 | 471.05 |
| 1010.6 | 484.71 |
| 1015.43 | 446.34 |
| 1007.21 | 469.02 |
| 1004.23 | 432.12 |
| 1015.51 | 467.28 |
| 1013.29 | 429.66 |
| 1013.16 | 469.49 |
| 1023.23 | 485.87 |
| 1026.0 | 481.95 |
| 1031.1 | 479.03 |
| 1010.99 | 434.5 |
| 1015.16 | 464.9 |
| 1020.02 | 452.71 |
| 1010.84 | 429.74 |
| 1020.67 | 457.09 |
| 1011.61 | 446.77 |
| 1014.46 | 460.76 |
| 1008.31 | 471.95 |
| 1014.09 | 453.29 |
| 1016.34 | 441.61 |
| 1017.01 | 464.73 |
| 1016.91 | 464.68 |
| 1003.72 | 430.59 |
| 1014.19 | 438.01 |
| 1021.84 | 479.08 |
| 1009.28 | 436.39 |
| 1016.25 | 447.07 |
| 1011.9 | 479.91 |
| 1013.88 | 489.05 |
| 1016.46 | 463.17 |
| 1001.18 | 471.26 |
| 1011.64 | 480.49 |
| 1025.41 | 473.78 |
| 1016.76 | 455.5 |
| 1013.78 | 446.27 |
| 1021.21 | 482.2 |
| 1016.72 | 452.48 |
| 1011.92 | 464.48 |
| 1015.29 | 438.1 |
| 1012.77 | 445.6 |
| 1010.37 | 442.43 |
| 1011.56 | 436.67 |
| 1016.54 | 466.56 |
| 1019.65 | 457.29 |
| 1029.65 | 487.03 |
| 1026.45 | 464.93 |
| 1019.28 | 466.0 |
| 1017.44 | 469.52 |
| 1011.18 | 428.88 |
| 1018.49 | 474.3 |
| 1009.69 | 461.06 |
| 1014.09 | 465.57 |
| 1012.3 | 467.67 |
| 1016.02 | 466.99 |
| 1013.73 | 463.72 |
| 1004.03 | 443.78 |
| 1005.43 | 445.23 |
| 1017.13 | 464.43 |
| 1028.31 | 484.36 |
| 1012.5 | 442.16 |
| 1004.64 | 464.11 |
| 1018.35 | 462.48 |
| 1009.59 | 477.49 |
| 1012.78 | 437.04 |
| 1003.8 | 457.09 |
| 1012.22 | 450.6 |
| 1023.25 | 465.78 |
| 1010.15 | 427.1 |
| 1020.14 | 459.81 |
| 1006.64 | 447.36 |
| 1011.0 | 488.92 |
| 1012.96 | 433.36 |
| 1021.99 | 483.35 |
| 1026.57 | 469.53 |
| 1012.27 | 476.96 |
| 1017.5 | 440.75 |
| 1024.51 | 462.55 |
| 1012.05 | 448.04 |
| 1016.22 | 455.24 |
| 1011.8 | 494.75 |
| 1012.55 | 444.58 |
| 1011.56 | 484.82 |
| 1019.6 | 442.9 |
| 1009.68 | 485.46 |
| 1012.82 | 457.81 |
| 1013.85 | 481.92 |
| 1009.63 | 443.23 |
| 1000.91 | 474.29 |
| 1009.98 | 430.46 |
| 1013.56 | 455.71 |
| 1011.25 | 438.34 |
| 1003.24 | 485.83 |
| 1017.76 | 452.82 |
| 1007.68 | 435.04 |
| 1016.82 | 451.21 |
| 1028.41 | 465.81 |
| 1025.04 | 458.42 |
| 1026.09 | 470.22 |
| 1020.84 | 449.24 |
| 1023.84 | 471.43 |
| 1023.74 | 473.26 |
| 1011.7 | 452.82 |
| 1008.1 | 432.69 |
| 1017.91 | 444.13 |
| 1029.8 | 467.21 |
| 1002.33 | 445.98 |
| 1002.42 | 436.91 |
| 1009.05 | 455.01 |
| 1002.47 | 437.11 |
| 1020.68 | 477.06 |
| 1006.65 | 441.71 |
| 1019.63 | 495.76 |
| 1011.33 | 445.63 |
| 1012.88 | 464.72 |
| 1005.94 | 438.03 |
| 1003.47 | 434.78 |
| 1015.63 | 444.67 |
| 1012.2 | 452.24 |
| 1014.19 | 450.92 |
| 1006.65 | 436.53 |
| 1005.75 | 435.53 |
| 1013.23 | 440.01 |
| 1008.72 | 443.1 |
| 1007.18 | 427.49 |
| 1012.99 | 436.25 |
| 1009.99 | 440.74 |
| 1015.02 | 443.54 |
| 1010.82 | 459.42 |
| 1009.76 | 439.66 |
| 1023.55 | 464.15 |
| 1020.55 | 459.1 |
| 1014.76 | 455.68 |
| 1015.33 | 469.08 |
| 1007.71 | 478.02 |
| 1017.36 | 456.8 |
| 1009.18 | 441.13 |
| 1017.05 | 463.88 |
| 1006.14 | 430.45 |
| 1014.24 | 449.18 |
| 1010.92 | 447.89 |
| 1010.4 | 431.59 |
| 1009.36 | 447.5 |
| 1033.04 | 475.58 |
| 1016.77 | 453.24 |
| 1012.59 | 446.4 |
| 1025.1 | 476.81 |
| 1019.29 | 474.1 |
| 1016.66 | 450.71 |
| 1006.4 | 433.62 |
| 1011.45 | 465.14 |
| 1019.08 | 445.18 |
| 1015.3 | 474.12 |
| 1016.08 | 483.91 |
| 1010.55 | 486.68 |
| 1022.43 | 464.98 |
| 1010.83 | 481.4 |
| 1021.81 | 479.2 |
| 1005.85 | 463.86 |
| 1012.76 | 472.3 |
| 1001.31 | 446.51 |
| 1005.93 | 437.71 |
| 1001.96 | 458.94 |
| 1007.62 | 437.91 |
| 1009.96 | 490.76 |
| 1013.4 | 439.66 |
| 1007.58 | 463.27 |
| 1016.68 | 473.99 |
| 1012.83 | 433.38 |
| 1015.13 | 459.01 |
| 1016.05 | 471.44 |
| 1012.97 | 471.91 |
| 1028.2 | 465.15 |
| 1008.25 | 446.66 |
| 1009.78 | 438.15 |
| 1008.81 | 447.14 |
| 1025.53 | 472.32 |
| 1010.16 | 441.68 |
| 1009.33 | 440.04 |
| 1009.82 | 444.82 |
| 1014.5 | 457.26 |
| 1009.13 | 428.83 |
| 1009.93 | 449.07 |
| 1009.38 | 435.21 |
| 1017.59 | 471.03 |
| 1012.47 | 465.56 |
| 1019.86 | 442.83 |
| 1017.26 | 460.3 |
| 1023.07 | 474.25 |
| 1033.3 | 477.97 |
| 1019.1 | 472.16 |
| 1014.22 | 456.08 |
| 1014.9 | 452.41 |
| 1011.31 | 463.71 |
| 1006.26 | 433.72 |
| 1016.0 | 456.4 |
| 1015.41 | 448.43 |
| 1020.63 | 481.6 |
| 1001.16 | 457.07 |
| 1019.8 | 451.0 |
| 1018.48 | 440.28 |
| 1002.26 | 437.47 |
| 1004.07 | 443.57 |
| 1004.91 | 426.6 |
| 1013.12 | 470.87 |
| 1012.9 | 478.37 |
| 1013.32 | 453.92 |
| 1020.79 | 470.22 |
| 1011.37 | 434.54 |
| 1013.11 | 442.89 |
| 1013.29 | 479.03 |
| 1020.5 | 476.06 |
| 1022.62 | 473.88 |
| 1010.84 | 451.75 |
| 1012.68 | 439.2 |
| 1015.58 | 439.7 |
| 1013.68 | 463.6 |
| 1004.21 | 447.47 |
| 1013.23 | 447.92 |
| 1020.44 | 471.08 |
| 1007.99 | 437.55 |
| 1012.36 | 448.27 |
| 998.47 | 431.69 |
| 1016.57 | 449.09 |
| 1015.93 | 448.79 |
| 1025.21 | 460.21 |
| 1013.54 | 479.28 |
| 1032.67 | 483.11 |
| 1011.46 | 450.75 |
| 1010.43 | 437.97 |
| 1008.53 | 459.76 |
| 1020.5 | 457.75 |
| 1015.48 | 469.33 |
| 1009.74 | 433.28 |
| 1010.23 | 444.64 |
| 1021.3 | 463.1 |
| 1022.01 | 460.91 |
| 1023.95 | 479.35 |
| 1017.65 | 449.23 |
| 1021.83 | 474.51 |
| 1007.81 | 435.02 |
| 1009.43 | 435.45 |
| 1013.3 | 452.38 |
| 1019.73 | 480.41 |
| 1019.54 | 478.96 |
| 1026.58 | 468.87 |
| 1007.89 | 434.01 |
| 1013.85 | 466.36 |
| 1011.44 | 435.28 |
| 1014.51 | 486.46 |
| 1015.51 | 468.19 |
| 1022.14 | 468.37 |
| 1019.17 | 474.19 |
| 1009.52 | 440.32 |
| 1015.35 | 485.32 |
| 1014.38 | 464.27 |
| 1013.66 | 479.25 |
| 1007.0 | 430.4 |
| 1016.65 | 447.49 |
| 1006.85 | 438.23 |
| 1025.46 | 492.09 |
| 1015.13 | 475.36 |
| 1016.68 | 452.56 |
| 1015.98 | 427.84 |
| 1010.8 | 433.95 |
| 1008.48 | 435.27 |
| 1014.04 | 454.62 |
| 1020.36 | 472.17 |
| 1015.96 | 452.42 |
| 1003.19 | 472.17 |
| 1018.01 | 481.83 |
| 1021.83 | 458.78 |
| 1022.47 | 447.5 |
| 1019.04 | 463.4 |
| 1022.67 | 473.57 |
| 1009.07 | 433.72 |
| 1011.2 | 431.85 |
| 1012.13 | 433.47 |
| 1007.45 | 432.84 |
| 1007.29 | 436.6 |
| 1020.12 | 490.23 |
| 1020.58 | 477.16 |
| 1010.44 | 441.06 |
| 1007.22 | 440.86 |
| 1033.08 | 477.94 |
| 1026.56 | 474.47 |
| 1012.18 | 470.67 |
| 1013.7 | 447.31 |
| 1018.14 | 466.8 |
| 1007.4 | 430.91 |
| 1007.2 | 434.75 |
| 1010.82 | 469.52 |
| 1008.88 | 438.9 |
| 1010.51 | 429.56 |
| 1013.53 | 432.92 |
| 1005.68 | 442.87 |
| 1022.57 | 466.59 |
| 1028.04 | 479.61 |
| 1019.12 | 471.08 |
| 1016.51 | 433.37 |
| 1018.8 | 443.92 |
| 1016.74 | 443.5 |
| 1017.37 | 439.89 |
| 1008.9 | 434.66 |
| 1021.95 | 487.57 |
| 1013.76 | 464.64 |
| 1017.26 | 470.92 |
| 1014.37 | 444.39 |
| 1002.54 | 442.48 |
| 1005.25 | 449.61 |
| 1009.43 | 435.02 |
| 1021.81 | 458.67 |
| 1016.63 | 461.74 |
| 1008.09 | 438.31 |
| 1011.68 | 462.38 |
| 1019.39 | 460.56 |
| 1013.37 | 439.22 |
| 1009.71 | 444.64 |
| 999.8 | 430.34 |
| 1008.37 | 430.46 |
| 1009.02 | 456.79 |
| 994.17 | 468.82 |
| 1008.79 | 448.51 |
| 1014.67 | 470.77 |
| 1025.79 | 465.74 |
| 1005.31 | 430.21 |
| 1008.62 | 449.23 |
| 1021.91 | 461.89 |
| 1010.97 | 445.72 |
| 1013.36 | 466.13 |
| 1008.38 | 448.71 |
| 998.43 | 469.25 |
| 1013.04 | 450.56 |
| 1008.94 | 464.46 |
| 1014.87 | 471.13 |
| 1010.92 | 461.52 |
| 1012.04 | 451.09 |
| 1005.43 | 431.51 |
| 1009.65 | 469.8 |
| 1012.05 | 442.28 |
| 1011.84 | 458.67 |
| 1016.13 | 462.4 |
| 1015.18 | 453.54 |
| 1019.81 | 444.38 |
| 1003.39 | 440.52 |
| 1007.68 | 433.62 |
| 1023.68 | 481.96 |
| 1008.89 | 452.75 |
| 1026.4 | 481.28 |
| 1020.28 | 439.03 |
| 1005.64 | 435.75 |
| 1009.72 | 436.03 |
| 1011.03 | 445.6 |
| 1020.27 | 462.65 |
| 1005.87 | 438.66 |
| 1008.54 | 447.32 |
| 1004.85 | 484.55 |
| 1024.9 | 476.8 |
| 1023.99 | 480.34 |
| 1010.53 | 440.63 |
| 1014.27 | 459.48 |
| 1025.98 | 490.78 |
| 1019.25 | 483.56 |
| 1012.26 | 429.38 |
| 1013.49 | 440.27 |
| 1011.34 | 445.34 |
| 1013.86 | 447.43 |
| 1004.37 | 439.91 |
| 1016.11 | 459.27 |
| 1017.88 | 478.89 |
| 1012.41 | 466.7 |
| 1021.39 | 463.5 |
| 1015.04 | 436.21 |
| 1011.95 | 443.94 |
| 1012.87 | 439.63 |
| 1013.21 | 460.95 |
| 1015.99 | 448.69 |
| 1006.24 | 444.63 |
| 1005.49 | 473.51 |
| 1008.56 | 462.56 |
| 1011.07 | 451.76 |
| 1025.68 | 491.81 |
| 1011.04 | 429.52 |
| 1007.59 | 437.9 |
| 1003.18 | 467.54 |
| 1015.35 | 449.97 |
| 1003.61 | 436.62 |
| 1023.37 | 477.68 |
| 1016.04 | 447.26 |
| 1011.8 | 439.76 |
| 1015.23 | 437.49 |
| 1018.29 | 455.14 |
| 1021.76 | 485.5 |
| 1016.81 | 444.1 |
| 1006.91 | 432.33 |
| 1008.87 | 471.23 |
| 1017.93 | 463.89 |
| 1009.2 | 445.54 |
| 1007.99 | 446.09 |
| 1017.82 | 445.12 |
| 1018.29 | 443.31 |
| 1015.14 | 484.16 |
| 1019.86 | 477.76 |
| 1008.36 | 430.28 |
| 1010.39 | 446.48 |
| 1028.11 | 481.03 |
| 1007.41 | 466.07 |
| 1013.2 | 447.47 |
| 1019.83 | 455.93 |
| 1021.15 | 479.62 |
| 1014.28 | 455.06 |
| 1019.87 | 475.06 |
| 1012.59 | 438.89 |
| 1003.38 | 432.7 |
| 1011.89 | 452.6 |
| 1010.46 | 451.75 |
| 1008.16 | 430.66 |
| 1019.04 | 491.9 |
| 1010.04 | 439.82 |
| 1015.91 | 460.73 |
| 1015.14 | 449.7 |
| 1013.88 | 439.42 |
| 1013.33 | 439.84 |
| 1025.58 | 485.86 |
| 1011.81 | 458.1 |
| 1012.89 | 479.92 |
| 1019.94 | 458.29 |
| 1017.29 | 489.45 |
| 1012.17 | 434.0 |
| 1010.69 | 431.24 |
| 1003.26 | 439.5 |
| 1019.48 | 467.46 |
| 1010.0 | 429.27 |
| 1016.95 | 452.1 |
| 999.83 | 472.41 |
| 1002.75 | 442.14 |
| 1003.56 | 441.0 |
| 1020.76 | 463.07 |
| 1017.99 | 445.71 |
| 1017.11 | 483.16 |
| 1010.75 | 440.45 |
| 1020.6 | 481.83 |
| 1015.53 | 467.6 |
| 1004.29 | 450.88 |
| 1001.22 | 425.5 |
| 1013.95 | 451.87 |
| 1010.51 | 428.94 |
| 1002.59 | 439.86 |
| 1011.21 | 433.44 |
| 1015.12 | 438.23 |
| 1007.68 | 436.95 |
| 1021.67 | 470.19 |
| 1011.6 | 484.66 |
| 1007.56 | 430.81 |
| 1010.05 | 433.37 |
| 1014.17 | 453.02 |
| 1012.6 | 453.5 |
| 995.88 | 463.09 |
| 1016.25 | 464.56 |
| 1017.22 | 452.12 |
| 1015.66 | 470.9 |
| 1021.08 | 450.89 |
| 1009.85 | 445.04 |
| 1011.49 | 444.72 |
| 1022.07 | 460.38 |
| 1013.05 | 446.8 |
| 1018.34 | 465.05 |
| 1016.55 | 484.13 |
| 1017.01 | 488.27 |
| 1013.85 | 447.09 |
| 1017.16 | 452.02 |
| 1014.16 | 455.55 |
| 1029.41 | 480.99 |
| 1012.96 | 467.68 |
| Humidity | Power |
|---|---|
| 73.17 | 463.26 |
| 59.08 | 444.37 |
| 92.14 | 488.56 |
| 76.64 | 446.48 |
| 96.62 | 473.9 |
| 58.77 | 443.67 |
| 75.24 | 467.35 |
| 66.43 | 478.42 |
| 41.25 | 475.98 |
| 70.72 | 477.5 |
| 75.04 | 453.02 |
| 64.22 | 453.99 |
| 84.15 | 440.29 |
| 61.83 | 451.28 |
| 87.59 | 433.99 |
| 43.08 | 462.19 |
| 48.84 | 467.54 |
| 77.51 | 477.2 |
| 63.59 | 459.85 |
| 55.28 | 464.3 |
| 66.26 | 468.27 |
| 64.77 | 495.24 |
| 83.31 | 483.8 |
| 47.19 | 443.61 |
| 54.93 | 436.06 |
| 74.62 | 443.25 |
| 72.52 | 464.16 |
| 88.44 | 475.52 |
| 92.28 | 484.41 |
| 41.85 | 437.89 |
| 44.28 | 445.11 |
| 64.58 | 438.86 |
| 63.25 | 440.98 |
| 78.61 | 436.65 |
| 44.51 | 444.26 |
| 89.46 | 465.86 |
| 74.52 | 444.37 |
| 88.86 | 450.69 |
| 75.51 | 469.02 |
| 78.64 | 448.86 |
| 76.65 | 447.14 |
| 80.44 | 469.18 |
| 79.89 | 482.8 |
| 88.28 | 476.7 |
| 84.6 | 474.99 |
| 42.69 | 444.22 |
| 78.41 | 461.33 |
| 61.07 | 448.06 |
| 50.0 | 474.6 |
| 77.29 | 473.05 |
| 43.66 | 432.06 |
| 83.8 | 467.41 |
| 66.47 | 430.12 |
| 93.09 | 473.62 |
| 80.52 | 471.81 |
| 68.99 | 442.99 |
| 57.27 | 442.77 |
| 95.53 | 491.49 |
| 71.72 | 447.46 |
| 57.88 | 446.11 |
| 63.34 | 442.44 |
| 48.07 | 446.22 |
| 91.87 | 471.49 |
| 87.27 | 463.5 |
| 64.4 | 440.01 |
| 43.4 | 441.03 |
| 72.24 | 452.68 |
| 90.22 | 474.91 |
| 74.0 | 478.77 |
| 71.85 | 434.2 |
| 86.62 | 437.91 |
| 97.41 | 477.61 |
| 84.44 | 431.65 |
| 81.55 | 430.57 |
| 75.66 | 481.09 |
| 79.41 | 445.56 |
| 58.91 | 475.74 |
| 90.06 | 435.12 |
| 79.0 | 446.15 |
| 69.47 | 436.64 |
| 51.47 | 436.69 |
| 83.13 | 468.75 |
| 40.33 | 466.6 |
| 81.69 | 465.48 |
| 94.55 | 441.34 |
| 91.81 | 441.83 |
| 63.62 | 464.7 |
| 49.35 | 437.99 |
| 69.61 | 459.12 |
| 38.75 | 429.69 |
| 90.17 | 459.8 |
| 81.24 | 433.63 |
| 48.46 | 442.84 |
| 76.72 | 485.13 |
| 51.16 | 459.12 |
| 76.34 | 445.31 |
| 67.3 | 480.8 |
| 52.38 | 432.55 |
| 76.44 | 443.86 |
| 91.55 | 449.77 |
| 71.9 | 470.71 |
| 80.05 | 452.17 |
| 63.77 | 478.29 |
| 62.26 | 428.54 |
| 89.04 | 478.27 |
| 58.02 | 439.58 |
| 81.82 | 457.32 |
| 91.14 | 475.51 |
| 88.92 | 439.66 |
| 84.83 | 471.99 |
| 91.76 | 479.81 |
| 86.56 | 434.78 |
| 57.21 | 446.58 |
| 54.25 | 437.76 |
| 63.8 | 459.36 |
| 33.71 | 462.28 |
| 67.25 | 464.33 |
| 60.11 | 444.36 |
| 74.55 | 438.64 |
| 67.34 | 470.49 |
| 42.75 | 455.13 |
| 55.2 | 450.22 |
| 83.61 | 440.43 |
| 88.78 | 482.98 |
| 100.12 | 460.44 |
| 64.52 | 444.97 |
| 51.41 | 433.94 |
| 85.78 | 439.73 |
| 75.41 | 434.48 |
| 81.63 | 442.33 |
| 51.92 | 457.67 |
| 70.12 | 454.66 |
| 53.83 | 432.21 |
| 77.23 | 457.66 |
| 65.67 | 435.21 |
| 71.18 | 448.22 |
| 81.96 | 475.51 |
| 79.54 | 446.53 |
| 47.09 | 441.3 |
| 57.69 | 433.54 |
| 78.89 | 472.52 |
| 85.29 | 474.77 |
| 40.13 | 435.1 |
| 77.06 | 450.74 |
| 67.38 | 442.7 |
| 62.44 | 426.56 |
| 77.43 | 463.71 |
| 58.77 | 447.06 |
| 67.72 | 452.27 |
| 42.14 | 445.78 |
| 84.16 | 438.65 |
| 89.79 | 480.15 |
| 67.21 | 447.19 |
| 72.14 | 443.04 |
| 97.49 | 488.81 |
| 87.74 | 455.75 |
| 96.3 | 455.86 |
| 61.25 | 457.68 |
| 88.38 | 479.11 |
| 74.77 | 432.84 |
| 68.18 | 448.37 |
| 77.2 | 447.06 |
| 49.54 | 443.53 |
| 92.22 | 445.21 |
| 33.65 | 441.7 |
| 64.59 | 450.93 |
| 100.09 | 451.44 |
| 68.04 | 441.29 |
| 48.94 | 458.85 |
| 74.47 | 481.46 |
| 81.02 | 467.19 |
| 71.17 | 461.54 |
| 53.85 | 439.08 |
| 70.67 | 467.22 |
| 59.36 | 468.8 |
| 57.17 | 426.93 |
| 70.29 | 474.65 |
| 83.37 | 468.97 |
| 87.36 | 433.97 |
| 100.09 | 450.53 |
| 68.78 | 444.51 |
| 70.98 | 469.03 |
| 75.68 | 466.56 |
| 47.49 | 457.57 |
| 71.99 | 440.13 |
| 66.55 | 433.24 |
| 74.73 | 452.55 |
| 64.78 | 443.29 |
| 75.13 | 431.76 |
| 56.38 | 454.97 |
| 94.35 | 456.7 |
| 86.55 | 486.03 |
| 82.95 | 472.79 |
| 88.42 | 452.03 |
| 85.61 | 443.41 |
| 58.39 | 441.93 |
| 74.28 | 432.64 |
| 87.85 | 480.25 |
| 83.5 | 466.68 |
| 65.24 | 494.39 |
| 75.01 | 454.72 |
| 84.52 | 448.71 |
| 80.52 | 469.76 |
| 75.14 | 450.71 |
| 75.75 | 444.01 |
| 76.72 | 453.2 |
| 85.47 | 450.87 |
| 57.95 | 441.73 |
| 78.32 | 465.09 |
| 52.2 | 447.28 |
| 93.69 | 491.16 |
| 75.74 | 450.98 |
| 67.56 | 446.3 |
| 69.46 | 436.48 |
| 74.58 | 460.84 |
| 53.23 | 442.56 |
| 88.72 | 467.3 |
| 96.16 | 479.13 |
| 68.26 | 441.15 |
| 86.39 | 445.52 |
| 85.34 | 475.4 |
| 72.64 | 469.3 |
| 97.82 | 463.57 |
| 77.22 | 445.32 |
| 80.59 | 461.03 |
| 46.91 | 466.74 |
| 57.76 | 444.04 |
| 53.09 | 434.01 |
| 84.31 | 465.23 |
| 71.58 | 440.6 |
| 92.97 | 466.74 |
| 74.55 | 433.48 |
| 78.96 | 473.59 |
| 64.44 | 474.81 |
| 68.23 | 454.75 |
| 70.81 | 452.94 |
| 61.66 | 435.83 |
| 77.76 | 482.19 |
| 69.49 | 466.66 |
| 96.26 | 462.59 |
| 55.74 | 447.82 |
| 95.61 | 462.73 |
| 84.75 | 447.98 |
| 75.3 | 462.72 |
| 67.5 | 442.42 |
| 80.92 | 444.69 |
| 79.23 | 466.7 |
| 81.1 | 453.84 |
| 32.8 | 436.92 |
| 84.31 | 486.37 |
| 46.15 | 440.43 |
| 53.96 | 446.82 |
| 59.83 | 484.91 |
| 75.3 | 437.76 |
| 42.53 | 438.91 |
| 70.58 | 464.19 |
| 91.69 | 442.19 |
| 63.55 | 446.86 |
| 61.51 | 457.15 |
| 69.55 | 482.57 |
| 98.08 | 476.03 |
| 79.34 | 428.89 |
| 81.28 | 472.7 |
| 78.99 | 445.6 |
| 80.38 | 464.78 |
| 51.16 | 440.42 |
| 72.17 | 428.41 |
| 75.39 | 438.5 |
| 68.91 | 438.28 |
| 96.38 | 476.29 |
| 70.54 | 448.46 |
| 45.8 | 438.99 |
| 57.95 | 471.8 |
| 81.89 | 471.81 |
| 69.32 | 449.82 |
| 59.14 | 442.14 |
| 81.54 | 441.46 |
| 85.81 | 477.62 |
| 65.41 | 446.76 |
| 81.15 | 472.52 |
| 95.87 | 471.58 |
| 90.24 | 440.85 |
| 75.13 | 431.37 |
| 88.22 | 437.33 |
| 81.48 | 469.22 |
| 89.84 | 471.11 |
| 43.57 | 439.17 |
| 63.16 | 445.33 |
| 57.14 | 473.71 |
| 77.76 | 452.66 |
| 90.56 | 440.99 |
| 60.98 | 467.42 |
| 70.31 | 444.14 |
| 74.05 | 457.17 |
| 75.42 | 467.87 |
| 82.25 | 442.04 |
| 67.95 | 471.36 |
| 100.09 | 460.7 |
| 47.28 | 431.33 |
| 72.41 | 432.6 |
| 77.67 | 447.61 |
| 63.7 | 443.87 |
| 79.77 | 446.87 |
| 93.84 | 465.74 |
| 84.95 | 447.86 |
| 70.16 | 447.65 |
| 84.24 | 437.87 |
| 73.32 | 483.51 |
| 86.17 | 479.65 |
| 65.43 | 455.16 |
| 94.59 | 431.91 |
| 86.8 | 470.68 |
| 58.18 | 429.28 |
| 89.66 | 450.81 |
| 87.39 | 437.73 |
| 36.35 | 460.21 |
| 79.62 | 442.86 |
| 50.52 | 482.99 |
| 51.96 | 440.0 |
| 74.73 | 478.48 |
| 78.33 | 455.28 |
| 85.19 | 436.94 |
| 83.13 | 461.06 |
| 53.49 | 438.28 |
| 88.86 | 472.61 |
| 60.89 | 426.85 |
| 61.14 | 470.18 |
| 68.29 | 455.38 |
| 57.62 | 428.32 |
| 83.63 | 480.35 |
| 78.1 | 455.56 |
| 66.34 | 447.66 |
| 79.02 | 443.06 |
| 68.96 | 452.43 |
| 71.13 | 477.81 |
| 87.01 | 431.66 |
| 74.3 | 431.8 |
| 77.62 | 446.67 |
| 59.56 | 445.26 |
| 41.66 | 425.72 |
| 73.27 | 430.58 |
| 77.16 | 439.86 |
| 67.02 | 441.11 |
| 52.8 | 434.72 |
| 39.04 | 434.01 |
| 65.47 | 475.64 |
| 74.32 | 460.44 |
| 69.22 | 436.4 |
| 93.88 | 461.03 |
| 69.83 | 479.08 |
| 84.11 | 435.76 |
| 78.65 | 460.14 |
| 69.31 | 442.2 |
| 70.3 | 447.69 |
| 68.23 | 431.15 |
| 71.76 | 445.0 |
| 85.88 | 431.59 |
| 71.09 | 467.22 |
| 52.67 | 445.33 |
| 89.68 | 470.57 |
| 73.66 | 473.77 |
| 58.94 | 447.67 |
| 87.05 | 474.29 |
| 67.0 | 437.14 |
| 43.18 | 432.56 |
| 80.62 | 459.14 |
| 59.72 | 446.19 |
| 72.1 | 428.1 |
| 69.15 | 468.46 |
| 55.66 | 435.02 |
| 61.19 | 445.52 |
| 74.62 | 462.69 |
| 73.35 | 455.75 |
| 68.85 | 463.74 |
| 39.89 | 439.79 |
| 53.16 | 443.26 |
| 52.97 | 432.04 |
| 79.87 | 465.86 |
| 84.09 | 465.6 |
| 100.15 | 469.43 |
| 79.77 | 440.75 |
| 88.99 | 481.32 |
| 76.14 | 479.87 |
| 69.13 | 458.59 |
| 93.03 | 438.62 |
| 77.92 | 445.59 |
| 74.89 | 481.87 |
| 88.7 | 475.01 |
| 62.94 | 436.54 |
| 89.62 | 456.63 |
| 81.04 | 451.69 |
| 94.53 | 463.04 |
| 64.02 | 446.1 |
| 70.57 | 438.67 |
| 70.32 | 466.88 |
| 84.86 | 444.6 |
| 81.41 | 440.26 |
| 89.45 | 483.92 |
| 82.71 | 475.19 |
| 93.93 | 479.24 |
| 70.6 | 434.92 |
| 87.68 | 454.16 |
| 87.58 | 447.58 |
| 74.4 | 467.9 |
| 67.35 | 426.29 |
| 63.61 | 447.02 |
| 76.89 | 455.85 |
| 78.08 | 476.46 |
| 69.17 | 437.48 |
| 53.31 | 452.77 |
| 93.32 | 491.54 |
| 42.47 | 438.41 |
| 82.58 | 476.1 |
| 94.59 | 464.58 |
| 86.31 | 467.74 |
| 72.57 | 442.12 |
| 80.76 | 453.34 |
| 71.93 | 425.29 |
| 47.54 | 449.63 |
| 95.72 | 462.88 |
| 77.03 | 464.67 |
| 80.49 | 489.96 |
| 77.67 | 482.38 |
| 78.72 | 437.95 |
| 58.77 | 429.2 |
| 74.8 | 453.34 |
| 51.34 | 442.47 |
| 90.41 | 462.6 |
| 91.1 | 478.79 |
| 62.57 | 456.11 |
| 84.27 | 450.33 |
| 42.93 | 434.83 |
| 40.96 | 433.43 |
| 76.53 | 456.02 |
| 69.74 | 485.23 |
| 74.99 | 473.57 |
| 70.45 | 469.94 |
| 91.49 | 452.07 |
| 88.97 | 475.32 |
| 89.13 | 480.69 |
| 46.52 | 444.01 |
| 60.55 | 465.17 |
| 88.71 | 480.61 |
| 89.15 | 476.04 |
| 83.02 | 441.76 |
| 75.19 | 428.24 |
| 87.35 | 444.77 |
| 85.66 | 463.1 |
| 91.66 | 470.5 |
| 63.47 | 431.0 |
| 72.25 | 430.68 |
| 70.58 | 436.42 |
| 60.1 | 452.33 |
| 89.29 | 440.16 |
| 67.43 | 435.75 |
| 67.58 | 449.74 |
| 70.8 | 430.73 |
| 63.62 | 432.75 |
| 66.68 | 446.79 |
| 90.76 | 486.35 |
| 75.34 | 453.18 |
| 59.77 | 458.31 |
| 80.79 | 480.26 |
| 74.1 | 448.65 |
| 41.34 | 458.41 |
| 58.78 | 435.39 |
| 97.78 | 450.21 |
| 74.85 | 459.59 |
| 69.84 | 445.84 |
| 75.36 | 441.08 |
| 85.8 | 467.33 |
| 90.11 | 444.19 |
| 61.63 | 432.96 |
| 44.76 | 438.09 |
| 89.7 | 467.9 |
| 72.51 | 475.72 |
| 74.98 | 477.51 |
| 79.59 | 435.13 |
| 78.42 | 477.9 |
| 61.23 | 457.26 |
| 47.56 | 467.53 |
| 93.06 | 465.15 |
| 89.65 | 474.28 |
| 50.5 | 444.49 |
| 44.84 | 452.84 |
| 85.32 | 435.38 |
| 82.94 | 433.57 |
| 67.26 | 435.27 |
| 79.05 | 468.49 |
| 62.03 | 433.07 |
| 94.36 | 430.63 |
| 60.02 | 440.74 |
| 95.46 | 474.49 |
| 84.92 | 449.74 |
| 62.8 | 436.73 |
| 90.81 | 434.58 |
| 80.9 | 473.93 |
| 55.84 | 435.99 |
| 75.3 | 466.83 |
| 37.34 | 427.22 |
| 79.5 | 444.07 |
| 87.29 | 469.57 |
| 81.5 | 459.89 |
| 76.42 | 479.59 |
| 75.75 | 440.92 |
| 84.95 | 480.87 |
| 62.37 | 441.9 |
| 49.25 | 430.2 |
| 74.16 | 465.16 |
| 90.55 | 471.32 |
| 94.28 | 485.43 |
| 63.31 | 495.35 |
| 78.9 | 449.12 |
| 90.97 | 480.53 |
| 87.34 | 457.07 |
| 80.8 | 443.67 |
| 90.95 | 477.52 |
| 69.97 | 472.95 |
| 89.45 | 472.54 |
| 76.08 | 469.17 |
| 83.35 | 435.21 |
| 92.16 | 477.78 |
| 58.42 | 475.89 |
| 85.06 | 483.9 |
| 88.91 | 476.2 |
| 83.33 | 462.16 |
| 88.49 | 471.05 |
| 96.88 | 484.71 |
| 73.86 | 446.34 |
| 65.17 | 469.02 |
| 69.41 | 432.12 |
| 81.23 | 467.28 |
| 54.07 | 429.66 |
| 89.17 | 469.49 |
| 78.85 | 485.87 |
| 84.44 | 481.95 |
| 83.02 | 479.03 |
| 90.66 | 434.5 |
| 75.29 | 464.9 |
| 82.6 | 452.71 |
| 45.4 | 429.74 |
| 66.33 | 457.09 |
| 45.33 | 446.77 |
| 67.12 | 460.76 |
| 84.14 | 471.95 |
| 80.81 | 453.29 |
| 49.13 | 441.61 |
| 87.29 | 464.73 |
| 52.95 | 464.68 |
| 68.92 | 430.59 |
| 85.21 | 438.01 |
| 88.56 | 479.08 |
| 55.09 | 436.39 |
| 48.64 | 447.07 |
| 87.85 | 479.91 |
| 87.42 | 489.05 |
| 62.75 | 463.17 |
| 94.86 | 471.26 |
| 63.54 | 480.49 |
| 69.46 | 473.78 |
| 74.66 | 455.5 |
| 80.57 | 446.27 |
| 84.7 | 482.2 |
| 72.6 | 452.48 |
| 52.63 | 464.48 |
| 82.01 | 438.1 |
| 75.22 | 445.6 |
| 51.05 | 442.43 |
| 80.1 | 436.67 |
| 81.58 | 466.56 |
| 65.94 | 457.29 |
| 86.74 | 487.03 |
| 62.57 | 464.93 |
| 57.37 | 466.0 |
| 88.91 | 469.52 |
| 72.26 | 428.88 |
| 74.98 | 474.3 |
| 71.19 | 461.06 |
| 62.82 | 465.57 |
| 55.31 | 467.67 |
| 71.57 | 466.99 |
| 59.16 | 463.72 |
| 40.8 | 443.78 |
| 67.63 | 445.23 |
| 97.2 | 464.43 |
| 91.16 | 484.36 |
| 64.81 | 442.16 |
| 85.61 | 464.11 |
| 93.42 | 462.48 |
| 77.36 | 477.49 |
| 67.03 | 437.04 |
| 89.45 | 457.09 |
| 54.84 | 450.6 |
| 53.48 | 465.78 |
| 54.47 | 427.1 |
| 43.36 | 459.81 |
| 48.92 | 447.36 |
| 81.22 | 488.92 |
| 60.35 | 433.36 |
| 75.98 | 483.35 |
| 74.24 | 469.53 |
| 85.21 | 476.96 |
| 68.46 | 440.75 |
| 78.31 | 462.55 |
| 89.25 | 448.04 |
| 68.57 | 455.24 |
| 67.38 | 494.75 |
| 53.6 | 444.58 |
| 91.69 | 484.82 |
| 78.21 | 442.9 |
| 94.19 | 485.46 |
| 37.19 | 457.81 |
| 83.53 | 481.92 |
| 79.45 | 443.23 |
| 99.9 | 474.29 |
| 50.39 | 430.46 |
| 74.33 | 455.71 |
| 83.66 | 438.34 |
| 89.48 | 485.83 |
| 64.59 | 452.82 |
| 75.68 | 435.04 |
| 64.18 | 451.21 |
| 70.09 | 465.81 |
| 70.58 | 458.42 |
| 99.28 | 470.22 |
| 81.89 | 449.24 |
| 87.99 | 471.43 |
| 88.21 | 473.26 |
| 91.29 | 452.82 |
| 52.72 | 432.69 |
| 67.5 | 444.13 |
| 92.05 | 467.21 |
| 63.23 | 445.98 |
| 90.88 | 436.91 |
| 74.91 | 455.01 |
| 85.39 | 437.11 |
| 96.98 | 477.06 |
| 56.28 | 441.71 |
| 65.62 | 495.76 |
| 55.32 | 445.63 |
| 88.88 | 464.72 |
| 39.49 | 438.03 |
| 54.59 | 434.78 |
| 57.19 | 444.67 |
| 45.06 | 452.24 |
| 40.62 | 450.92 |
| 90.21 | 436.53 |
| 90.91 | 435.53 |
| 74.96 | 440.01 |
| 54.21 | 443.1 |
| 63.62 | 427.49 |
| 50.04 | 436.25 |
| 51.23 | 440.74 |
| 82.71 | 443.54 |
| 92.04 | 459.42 |
| 90.67 | 439.66 |
| 91.14 | 464.15 |
| 70.43 | 459.1 |
| 66.63 | 455.68 |
| 86.95 | 469.08 |
| 96.69 | 478.02 |
| 70.88 | 456.8 |
| 47.14 | 441.13 |
| 63.36 | 463.88 |
| 60.58 | 430.45 |
| 54.3 | 449.18 |
| 65.09 | 447.89 |
| 48.16 | 431.59 |
| 81.51 | 447.5 |
| 68.57 | 475.58 |
| 73.16 | 453.24 |
| 80.88 | 446.4 |
| 85.4 | 476.81 |
| 75.77 | 474.1 |
| 75.76 | 450.71 |
| 70.21 | 433.62 |
| 55.53 | 465.14 |
| 80.48 | 445.18 |
| 72.41 | 474.12 |
| 83.25 | 483.91 |
| 82.12 | 486.68 |
| 94.75 | 464.98 |
| 95.79 | 481.4 |
| 86.02 | 479.2 |
| 78.29 | 463.86 |
| 82.23 | 472.3 |
| 52.86 | 446.51 |
| 60.66 | 437.71 |
| 62.77 | 458.94 |
| 65.54 | 437.91 |
| 95.4 | 490.76 |
| 51.78 | 439.66 |
| 63.62 | 463.27 |
| 83.09 | 473.99 |
| 61.81 | 433.38 |
| 68.24 | 459.01 |
| 72.41 | 471.44 |
| 79.64 | 471.91 |
| 66.95 | 465.15 |
| 91.98 | 446.66 |
| 64.96 | 438.15 |
| 88.93 | 447.14 |
| 85.62 | 472.32 |
| 84.0 | 441.68 |
| 89.41 | 440.04 |
| 67.4 | 444.82 |
| 76.75 | 457.26 |
| 89.06 | 428.83 |
| 64.02 | 449.07 |
| 64.12 | 435.21 |
| 81.22 | 471.03 |
| 100.13 | 465.56 |
| 58.07 | 442.83 |
| 63.42 | 460.3 |
| 83.32 | 474.25 |
| 74.28 | 477.97 |
| 71.91 | 472.16 |
| 85.8 | 456.08 |
| 55.58 | 452.41 |
| 69.7 | 463.71 |
| 63.79 | 433.72 |
| 86.59 | 456.4 |
| 48.28 | 448.43 |
| 80.42 | 481.6 |
| 98.58 | 457.07 |
| 72.83 | 451.0 |
| 56.07 | 440.28 |
| 67.13 | 437.47 |
| 84.49 | 443.57 |
| 68.37 | 426.6 |
| 86.07 | 470.87 |
| 83.82 | 478.37 |
| 74.86 | 453.92 |
| 53.52 | 470.22 |
| 80.61 | 434.54 |
| 43.56 | 442.89 |
| 89.35 | 479.03 |
| 97.28 | 476.06 |
| 80.49 | 473.88 |
| 88.9 | 451.75 |
| 49.7 | 439.2 |
| 68.64 | 439.7 |
| 98.58 | 463.6 |
| 82.12 | 447.47 |
| 78.32 | 447.92 |
| 86.04 | 471.08 |
| 91.36 | 437.55 |
| 81.02 | 448.27 |
| 76.05 | 431.69 |
| 71.81 | 449.09 |
| 82.13 | 448.79 |
| 74.27 | 460.21 |
| 71.32 | 479.28 |
| 74.59 | 483.11 |
| 84.44 | 450.75 |
| 43.39 | 437.97 |
| 87.2 | 459.76 |
| 77.11 | 457.75 |
| 82.81 | 469.33 |
| 85.67 | 433.28 |
| 95.58 | 444.64 |
| 74.46 | 463.1 |
| 90.02 | 460.91 |
| 81.93 | 479.35 |
| 86.29 | 449.23 |
| 85.43 | 474.51 |
| 71.66 | 435.02 |
| 71.33 | 435.45 |
| 67.72 | 452.38 |
| 84.23 | 480.41 |
| 74.44 | 478.96 |
| 71.48 | 468.87 |
| 56.3 | 434.01 |
| 68.13 | 466.36 |
| 68.35 | 435.28 |
| 85.23 | 486.46 |
| 79.78 | 468.19 |
| 98.98 | 468.37 |
| 72.87 | 474.19 |
| 90.93 | 440.32 |
| 72.94 | 485.32 |
| 72.3 | 464.27 |
| 77.74 | 479.25 |
| 78.29 | 430.4 |
| 69.1 | 447.49 |
| 55.79 | 438.23 |
| 75.09 | 492.09 |
| 88.98 | 475.36 |
| 64.26 | 452.56 |
| 25.89 | 427.84 |
| 59.18 | 433.95 |
| 67.48 | 435.27 |
| 89.85 | 454.62 |
| 50.62 | 472.17 |
| 83.97 | 452.42 |
| 96.51 | 472.17 |
| 80.09 | 481.83 |
| 84.02 | 458.78 |
| 61.97 | 447.5 |
| 88.51 | 463.4 |
| 81.83 | 473.57 |
| 90.63 | 433.72 |
| 73.37 | 431.85 |
| 77.5 | 433.47 |
| 57.46 | 432.84 |
| 51.91 | 436.6 |
| 79.14 | 490.23 |
| 69.24 | 477.16 |
| 41.85 | 441.06 |
| 95.1 | 440.86 |
| 74.53 | 477.94 |
| 64.85 | 474.47 |
| 57.07 | 470.67 |
| 62.9 | 447.31 |
| 72.21 | 466.8 |
| 65.99 | 430.91 |
| 73.67 | 434.75 |
| 88.59 | 469.52 |
| 61.19 | 438.9 |
| 49.37 | 429.56 |
| 48.65 | 432.92 |
| 56.18 | 442.87 |
| 71.56 | 466.59 |
| 87.46 | 479.61 |
| 70.02 | 471.08 |
| 61.2 | 433.37 |
| 60.54 | 443.92 |
| 71.82 | 443.5 |
| 44.8 | 439.89 |
| 67.32 | 434.66 |
| 78.77 | 487.57 |
| 96.02 | 464.64 |
| 90.56 | 470.92 |
| 83.19 | 444.39 |
| 68.45 | 442.48 |
| 99.19 | 449.61 |
| 88.11 | 435.02 |
| 79.29 | 458.67 |
| 87.76 | 461.74 |
| 82.56 | 438.31 |
| 79.24 | 462.38 |
| 67.24 | 460.56 |
| 58.98 | 439.22 |
| 84.22 | 444.64 |
| 89.12 | 430.34 |
| 50.07 | 430.46 |
| 98.86 | 456.79 |
| 95.79 | 468.82 |
| 70.06 | 448.51 |
| 41.71 | 470.77 |
| 86.55 | 465.74 |
| 71.97 | 430.21 |
| 96.4 | 449.23 |
| 91.73 | 461.89 |
| 91.62 | 445.72 |
| 59.14 | 466.13 |
| 92.56 | 448.71 |
| 83.71 | 469.25 |
| 55.43 | 450.56 |
| 74.91 | 464.46 |
| 89.41 | 471.13 |
| 69.81 | 461.52 |
| 86.01 | 451.09 |
| 86.05 | 431.51 |
| 80.98 | 469.8 |
| 63.62 | 442.28 |
| 64.16 | 458.67 |
| 75.63 | 462.4 |
| 80.21 | 453.54 |
| 59.7 | 444.38 |
| 47.6 | 440.52 |
| 63.78 | 433.62 |
| 89.37 | 481.96 |
| 70.55 | 452.75 |
| 84.42 | 481.28 |
| 80.62 | 439.03 |
| 52.56 | 435.75 |
| 83.26 | 436.03 |
| 70.64 | 445.6 |
| 89.95 | 462.65 |
| 51.53 | 438.66 |
| 84.83 | 447.32 |
| 59.68 | 484.55 |
| 97.88 | 476.8 |
| 85.03 | 480.34 |
| 47.38 | 440.63 |
| 48.08 | 459.48 |
| 79.65 | 490.78 |
| 83.39 | 483.56 |
| 82.18 | 429.38 |
| 51.71 | 440.27 |
| 77.33 | 445.34 |
| 72.81 | 447.43 |
| 84.26 | 439.91 |
| 73.23 | 459.27 |
| 79.73 | 478.89 |
| 62.32 | 466.7 |
| 78.58 | 463.5 |
| 79.88 | 436.21 |
| 65.87 | 443.94 |
| 80.28 | 439.63 |
| 71.33 | 460.95 |
| 70.33 | 448.69 |
| 57.73 | 444.63 |
| 99.46 | 473.51 |
| 68.61 | 462.56 |
| 95.91 | 451.76 |
| 80.42 | 491.81 |
| 51.01 | 429.52 |
| 74.08 | 437.9 |
| 80.73 | 467.54 |
| 54.71 | 449.97 |
| 73.75 | 436.62 |
| 88.43 | 477.68 |
| 74.66 | 447.26 |
| 70.04 | 439.76 |
| 74.64 | 437.49 |
| 85.11 | 455.14 |
| 82.97 | 485.5 |
| 55.59 | 444.1 |
| 49.9 | 432.33 |
| 89.99 | 471.23 |
| 91.61 | 463.89 |
| 82.95 | 445.54 |
| 92.62 | 446.09 |
| 59.64 | 445.12 |
| 63.0 | 443.31 |
| 85.38 | 484.16 |
| 85.23 | 477.76 |
| 52.08 | 430.28 |
| 38.05 | 446.48 |
| 71.98 | 481.03 |
| 90.66 | 466.07 |
| 83.14 | 447.47 |
| 65.22 | 455.93 |
| 91.67 | 479.62 |
| 66.04 | 455.06 |
| 78.19 | 475.06 |
| 54.47 | 438.89 |
| 67.26 | 432.7 |
| 72.56 | 452.6 |
| 82.15 | 451.75 |
| 86.32 | 430.66 |
| 88.17 | 491.9 |
| 72.78 | 439.82 |
| 69.58 | 460.73 |
| 69.86 | 449.7 |
| 65.37 | 439.42 |
| 52.37 | 439.84 |
| 79.63 | 485.86 |
| 83.14 | 458.1 |
| 88.25 | 479.92 |
| 55.85 | 458.29 |
| 52.55 | 489.45 |
| 62.74 | 434.0 |
| 90.08 | 431.24 |
| 54.5 | 439.5 |
| 49.88 | 467.46 |
| 48.96 | 429.27 |
| 86.77 | 452.1 |
| 96.66 | 472.41 |
| 70.84 | 442.14 |
| 83.83 | 441.0 |
| 68.22 | 463.07 |
| 82.22 | 445.71 |
| 87.9 | 483.16 |
| 66.83 | 440.45 |
| 85.36 | 481.83 |
| 60.9 | 467.6 |
| 83.51 | 450.88 |
| 52.96 | 425.5 |
| 73.02 | 451.87 |
| 43.11 | 428.94 |
| 61.41 | 439.86 |
| 65.32 | 433.44 |
| 93.68 | 438.23 |
| 42.39 | 436.95 |
| 68.18 | 470.19 |
| 89.18 | 484.66 |
| 64.79 | 430.81 |
| 43.48 | 433.37 |
| 80.4 | 453.02 |
| 72.43 | 453.5 |
| 80.0 | 463.09 |
| 45.65 | 464.56 |
| 63.02 | 452.12 |
| 74.39 | 470.9 |
| 57.77 | 450.89 |
| 76.8 | 445.04 |
| 67.39 | 444.72 |
| 73.96 | 460.38 |
| 72.75 | 446.8 |
| 71.69 | 465.05 |
| 84.98 | 484.13 |
| 87.68 | 488.27 |
| 50.36 | 447.09 |
| 68.11 | 452.02 |
| 66.27 | 455.55 |
| 89.72 | 480.99 |
| 61.07 | 467.68 |
| RH | PE |
|---|---|
| 73.17 | 463.26 |
| 59.08 | 444.37 |
| 92.14 | 488.56 |
| 76.64 | 446.48 |
| 96.62 | 473.9 |
| 58.77 | 443.67 |
| 75.24 | 467.35 |
| 66.43 | 478.42 |
| 41.25 | 475.98 |
| 70.72 | 477.5 |
| 75.04 | 453.02 |
| 64.22 | 453.99 |
| 84.15 | 440.29 |
| 61.83 | 451.28 |
| 87.59 | 433.99 |
| 43.08 | 462.19 |
| 48.84 | 467.54 |
| 77.51 | 477.2 |
| 63.59 | 459.85 |
| 55.28 | 464.3 |
| 66.26 | 468.27 |
| 64.77 | 495.24 |
| 83.31 | 483.8 |
| 47.19 | 443.61 |
| 54.93 | 436.06 |
| 74.62 | 443.25 |
| 72.52 | 464.16 |
| 88.44 | 475.52 |
| 92.28 | 484.41 |
| 41.85 | 437.89 |
| 44.28 | 445.11 |
| 64.58 | 438.86 |
| 63.25 | 440.98 |
| 78.61 | 436.65 |
| 44.51 | 444.26 |
| 89.46 | 465.86 |
| 74.52 | 444.37 |
| 88.86 | 450.69 |
| 75.51 | 469.02 |
| 78.64 | 448.86 |
| 76.65 | 447.14 |
| 80.44 | 469.18 |
| 79.89 | 482.8 |
| 88.28 | 476.7 |
| 84.6 | 474.99 |
| 42.69 | 444.22 |
| 78.41 | 461.33 |
| 61.07 | 448.06 |
| 50.0 | 474.6 |
| 77.29 | 473.05 |
| 43.66 | 432.06 |
| 83.8 | 467.41 |
| 66.47 | 430.12 |
| 93.09 | 473.62 |
| 80.52 | 471.81 |
| 68.99 | 442.99 |
| 57.27 | 442.77 |
| 95.53 | 491.49 |
| 71.72 | 447.46 |
| 57.88 | 446.11 |
| 63.34 | 442.44 |
| 48.07 | 446.22 |
| 91.87 | 471.49 |
| 87.27 | 463.5 |
| 64.4 | 440.01 |
| 43.4 | 441.03 |
| 72.24 | 452.68 |
| 90.22 | 474.91 |
| 74.0 | 478.77 |
| 71.85 | 434.2 |
| 86.62 | 437.91 |
| 97.41 | 477.61 |
| 84.44 | 431.65 |
| 81.55 | 430.57 |
| 75.66 | 481.09 |
| 79.41 | 445.56 |
| 58.91 | 475.74 |
| 90.06 | 435.12 |
| 79.0 | 446.15 |
| 69.47 | 436.64 |
| 51.47 | 436.69 |
| 83.13 | 468.75 |
| 40.33 | 466.6 |
| 81.69 | 465.48 |
| 94.55 | 441.34 |
| 91.81 | 441.83 |
| 63.62 | 464.7 |
| 49.35 | 437.99 |
| 69.61 | 459.12 |
| 38.75 | 429.69 |
| 90.17 | 459.8 |
| 81.24 | 433.63 |
| 48.46 | 442.84 |
| 76.72 | 485.13 |
| 51.16 | 459.12 |
| 76.34 | 445.31 |
| 67.3 | 480.8 |
| 52.38 | 432.55 |
| 76.44 | 443.86 |
| 91.55 | 449.77 |
| 71.9 | 470.71 |
| 80.05 | 452.17 |
| 63.77 | 478.29 |
| 62.26 | 428.54 |
| 89.04 | 478.27 |
| 58.02 | 439.58 |
| 81.82 | 457.32 |
| 91.14 | 475.51 |
| 88.92 | 439.66 |
| 84.83 | 471.99 |
| 91.76 | 479.81 |
| 86.56 | 434.78 |
| 57.21 | 446.58 |
| 54.25 | 437.76 |
| 63.8 | 459.36 |
| 33.71 | 462.28 |
| 67.25 | 464.33 |
| 60.11 | 444.36 |
| 74.55 | 438.64 |
| 67.34 | 470.49 |
| 42.75 | 455.13 |
| 55.2 | 450.22 |
| 83.61 | 440.43 |
| 88.78 | 482.98 |
| 100.12 | 460.44 |
| 64.52 | 444.97 |
| 51.41 | 433.94 |
| 85.78 | 439.73 |
| 75.41 | 434.48 |
| 81.63 | 442.33 |
| 51.92 | 457.67 |
| 70.12 | 454.66 |
| 53.83 | 432.21 |
| 77.23 | 457.66 |
| 65.67 | 435.21 |
| 71.18 | 448.22 |
| 81.96 | 475.51 |
| 79.54 | 446.53 |
| 47.09 | 441.3 |
| 57.69 | 433.54 |
| 78.89 | 472.52 |
| 85.29 | 474.77 |
| 40.13 | 435.1 |
| 77.06 | 450.74 |
| 67.38 | 442.7 |
| 62.44 | 426.56 |
| 77.43 | 463.71 |
| 58.77 | 447.06 |
| 67.72 | 452.27 |
| 42.14 | 445.78 |
| 84.16 | 438.65 |
| 89.79 | 480.15 |
| 67.21 | 447.19 |
| 72.14 | 443.04 |
| 97.49 | 488.81 |
| 87.74 | 455.75 |
| 96.3 | 455.86 |
| 61.25 | 457.68 |
| 88.38 | 479.11 |
| 74.77 | 432.84 |
| 68.18 | 448.37 |
| 77.2 | 447.06 |
| 49.54 | 443.53 |
| 92.22 | 445.21 |
| 33.65 | 441.7 |
| 64.59 | 450.93 |
| 100.09 | 451.44 |
| 68.04 | 441.29 |
| 48.94 | 458.85 |
| 74.47 | 481.46 |
| 81.02 | 467.19 |
| 71.17 | 461.54 |
| 53.85 | 439.08 |
| 70.67 | 467.22 |
| 59.36 | 468.8 |
| 57.17 | 426.93 |
| 70.29 | 474.65 |
| 83.37 | 468.97 |
| 87.36 | 433.97 |
| 100.09 | 450.53 |
| 68.78 | 444.51 |
| 70.98 | 469.03 |
| 75.68 | 466.56 |
| 47.49 | 457.57 |
| 71.99 | 440.13 |
| 66.55 | 433.24 |
| 74.73 | 452.55 |
| 64.78 | 443.29 |
| 75.13 | 431.76 |
| 56.38 | 454.97 |
| 94.35 | 456.7 |
| 86.55 | 486.03 |
| 82.95 | 472.79 |
| 88.42 | 452.03 |
| 85.61 | 443.41 |
| 58.39 | 441.93 |
| 74.28 | 432.64 |
| 87.85 | 480.25 |
| 83.5 | 466.68 |
| 65.24 | 494.39 |
| 75.01 | 454.72 |
| 84.52 | 448.71 |
| 80.52 | 469.76 |
| 75.14 | 450.71 |
| 75.75 | 444.01 |
| 76.72 | 453.2 |
| 85.47 | 450.87 |
| 57.95 | 441.73 |
| 78.32 | 465.09 |
| 52.2 | 447.28 |
| 93.69 | 491.16 |
| 75.74 | 450.98 |
| 67.56 | 446.3 |
| 69.46 | 436.48 |
| 74.58 | 460.84 |
| 53.23 | 442.56 |
| 88.72 | 467.3 |
| 96.16 | 479.13 |
| 68.26 | 441.15 |
| 86.39 | 445.52 |
| 85.34 | 475.4 |
| 72.64 | 469.3 |
| 97.82 | 463.57 |
| 77.22 | 445.32 |
| 80.59 | 461.03 |
| 46.91 | 466.74 |
| 57.76 | 444.04 |
| 53.09 | 434.01 |
| 84.31 | 465.23 |
| 71.58 | 440.6 |
| 92.97 | 466.74 |
| 74.55 | 433.48 |
| 78.96 | 473.59 |
| 64.44 | 474.81 |
| 68.23 | 454.75 |
| 70.81 | 452.94 |
| 61.66 | 435.83 |
| 77.76 | 482.19 |
| 69.49 | 466.66 |
| 96.26 | 462.59 |
| 55.74 | 447.82 |
| 95.61 | 462.73 |
| 84.75 | 447.98 |
| 75.3 | 462.72 |
| 67.5 | 442.42 |
| 80.92 | 444.69 |
| 79.23 | 466.7 |
| 81.1 | 453.84 |
| 32.8 | 436.92 |
| 84.31 | 486.37 |
| 46.15 | 440.43 |
| 53.96 | 446.82 |
| 59.83 | 484.91 |
| 75.3 | 437.76 |
| 42.53 | 438.91 |
| 70.58 | 464.19 |
| 91.69 | 442.19 |
| 63.55 | 446.86 |
| 61.51 | 457.15 |
| 69.55 | 482.57 |
| 98.08 | 476.03 |
| 79.34 | 428.89 |
| 81.28 | 472.7 |
| 78.99 | 445.6 |
| 80.38 | 464.78 |
| 51.16 | 440.42 |
| 72.17 | 428.41 |
| 75.39 | 438.5 |
| 68.91 | 438.28 |
| 96.38 | 476.29 |
| 70.54 | 448.46 |
| 45.8 | 438.99 |
| 57.95 | 471.8 |
| 81.89 | 471.81 |
| 69.32 | 449.82 |
| 59.14 | 442.14 |
| 81.54 | 441.46 |
| 85.81 | 477.62 |
| 65.41 | 446.76 |
| 81.15 | 472.52 |
| 95.87 | 471.58 |
| 90.24 | 440.85 |
| 75.13 | 431.37 |
| 88.22 | 437.33 |
| 81.48 | 469.22 |
| 89.84 | 471.11 |
| 43.57 | 439.17 |
| 63.16 | 445.33 |
| 57.14 | 473.71 |
| 77.76 | 452.66 |
| 90.56 | 440.99 |
| 60.98 | 467.42 |
| 70.31 | 444.14 |
| 74.05 | 457.17 |
| 75.42 | 467.87 |
| 82.25 | 442.04 |
| 67.95 | 471.36 |
| 100.09 | 460.7 |
| 47.28 | 431.33 |
| 72.41 | 432.6 |
| 77.67 | 447.61 |
| 63.7 | 443.87 |
| 79.77 | 446.87 |
| 93.84 | 465.74 |
| 84.95 | 447.86 |
| 70.16 | 447.65 |
| 84.24 | 437.87 |
| 73.32 | 483.51 |
| 86.17 | 479.65 |
| 65.43 | 455.16 |
| 94.59 | 431.91 |
| 86.8 | 470.68 |
| 58.18 | 429.28 |
| 89.66 | 450.81 |
| 87.39 | 437.73 |
| 36.35 | 460.21 |
| 79.62 | 442.86 |
| 50.52 | 482.99 |
| 51.96 | 440.0 |
| 74.73 | 478.48 |
| 78.33 | 455.28 |
| 85.19 | 436.94 |
| 83.13 | 461.06 |
| 53.49 | 438.28 |
| 88.86 | 472.61 |
| 60.89 | 426.85 |
| 61.14 | 470.18 |
| 68.29 | 455.38 |
| 57.62 | 428.32 |
| 83.63 | 480.35 |
| 78.1 | 455.56 |
| 66.34 | 447.66 |
| 79.02 | 443.06 |
| 68.96 | 452.43 |
| 71.13 | 477.81 |
| 87.01 | 431.66 |
| 74.3 | 431.8 |
| 77.62 | 446.67 |
| 59.56 | 445.26 |
| 41.66 | 425.72 |
| 73.27 | 430.58 |
| 77.16 | 439.86 |
| 67.02 | 441.11 |
| 52.8 | 434.72 |
| 39.04 | 434.01 |
| 65.47 | 475.64 |
| 74.32 | 460.44 |
| 69.22 | 436.4 |
| 93.88 | 461.03 |
| 69.83 | 479.08 |
| 84.11 | 435.76 |
| 78.65 | 460.14 |
| 69.31 | 442.2 |
| 70.3 | 447.69 |
| 68.23 | 431.15 |
| 71.76 | 445.0 |
| 85.88 | 431.59 |
| 71.09 | 467.22 |
| 52.67 | 445.33 |
| 89.68 | 470.57 |
| 73.66 | 473.77 |
| 58.94 | 447.67 |
| 87.05 | 474.29 |
| 67.0 | 437.14 |
| 43.18 | 432.56 |
| 80.62 | 459.14 |
| 59.72 | 446.19 |
| 72.1 | 428.1 |
| 69.15 | 468.46 |
| 55.66 | 435.02 |
| 61.19 | 445.52 |
| 74.62 | 462.69 |
| 73.35 | 455.75 |
| 68.85 | 463.74 |
| 39.89 | 439.79 |
| 53.16 | 443.26 |
| 52.97 | 432.04 |
| 79.87 | 465.86 |
| 84.09 | 465.6 |
| 100.15 | 469.43 |
| 79.77 | 440.75 |
| 88.99 | 481.32 |
| 76.14 | 479.87 |
| 69.13 | 458.59 |
| 93.03 | 438.62 |
| 77.92 | 445.59 |
| 74.89 | 481.87 |
| 88.7 | 475.01 |
| 62.94 | 436.54 |
| 89.62 | 456.63 |
| 81.04 | 451.69 |
| 94.53 | 463.04 |
| 64.02 | 446.1 |
| 70.57 | 438.67 |
| 70.32 | 466.88 |
| 84.86 | 444.6 |
| 81.41 | 440.26 |
| 89.45 | 483.92 |
| 82.71 | 475.19 |
| 93.93 | 479.24 |
| 70.6 | 434.92 |
| 87.68 | 454.16 |
| 87.58 | 447.58 |
| 74.4 | 467.9 |
| 67.35 | 426.29 |
| 63.61 | 447.02 |
| 76.89 | 455.85 |
| 78.08 | 476.46 |
| 69.17 | 437.48 |
| 53.31 | 452.77 |
| 93.32 | 491.54 |
| 42.47 | 438.41 |
| 82.58 | 476.1 |
| 94.59 | 464.58 |
| 86.31 | 467.74 |
| 72.57 | 442.12 |
| 80.76 | 453.34 |
| 71.93 | 425.29 |
| 47.54 | 449.63 |
| 95.72 | 462.88 |
| 77.03 | 464.67 |
| 80.49 | 489.96 |
| 77.67 | 482.38 |
| 78.72 | 437.95 |
| 58.77 | 429.2 |
| 74.8 | 453.34 |
| 51.34 | 442.47 |
| 90.41 | 462.6 |
| 91.1 | 478.79 |
| 62.57 | 456.11 |
| 84.27 | 450.33 |
| 42.93 | 434.83 |
| 40.96 | 433.43 |
| 76.53 | 456.02 |
| 69.74 | 485.23 |
| 74.99 | 473.57 |
| 70.45 | 469.94 |
| 91.49 | 452.07 |
| 88.97 | 475.32 |
| 89.13 | 480.69 |
| 46.52 | 444.01 |
| 60.55 | 465.17 |
| 88.71 | 480.61 |
| 89.15 | 476.04 |
| 83.02 | 441.76 |
| 75.19 | 428.24 |
| 87.35 | 444.77 |
| 85.66 | 463.1 |
| 91.66 | 470.5 |
| 63.47 | 431.0 |
| 72.25 | 430.68 |
| 70.58 | 436.42 |
| 60.1 | 452.33 |
| 89.29 | 440.16 |
| 67.43 | 435.75 |
| 67.58 | 449.74 |
| 70.8 | 430.73 |
| 63.62 | 432.75 |
| 66.68 | 446.79 |
| 90.76 | 486.35 |
| 75.34 | 453.18 |
| 59.77 | 458.31 |
| 80.79 | 480.26 |
| 74.1 | 448.65 |
| 41.34 | 458.41 |
| 58.78 | 435.39 |
| 97.78 | 450.21 |
| 74.85 | 459.59 |
| 69.84 | 445.84 |
| 75.36 | 441.08 |
| 85.8 | 467.33 |
| 90.11 | 444.19 |
| 61.63 | 432.96 |
| 44.76 | 438.09 |
| 89.7 | 467.9 |
| 72.51 | 475.72 |
| 74.98 | 477.51 |
| 79.59 | 435.13 |
| 78.42 | 477.9 |
| 61.23 | 457.26 |
| 47.56 | 467.53 |
| 93.06 | 465.15 |
| 89.65 | 474.28 |
| 50.5 | 444.49 |
| 44.84 | 452.84 |
| 85.32 | 435.38 |
| 82.94 | 433.57 |
| 67.26 | 435.27 |
| 79.05 | 468.49 |
| 62.03 | 433.07 |
| 94.36 | 430.63 |
| 60.02 | 440.74 |
| 95.46 | 474.49 |
| 84.92 | 449.74 |
| 62.8 | 436.73 |
| 90.81 | 434.58 |
| 80.9 | 473.93 |
| 55.84 | 435.99 |
| 75.3 | 466.83 |
| 37.34 | 427.22 |
| 79.5 | 444.07 |
| 87.29 | 469.57 |
| 81.5 | 459.89 |
| 76.42 | 479.59 |
| 75.75 | 440.92 |
| 84.95 | 480.87 |
| 62.37 | 441.9 |
| 49.25 | 430.2 |
| 74.16 | 465.16 |
| 90.55 | 471.32 |
| 94.28 | 485.43 |
| 63.31 | 495.35 |
| 78.9 | 449.12 |
| 90.97 | 480.53 |
| 87.34 | 457.07 |
| 80.8 | 443.67 |
| 90.95 | 477.52 |
| 69.97 | 472.95 |
| 89.45 | 472.54 |
| 76.08 | 469.17 |
| 83.35 | 435.21 |
| 92.16 | 477.78 |
| 58.42 | 475.89 |
| 85.06 | 483.9 |
| 88.91 | 476.2 |
| 83.33 | 462.16 |
| 88.49 | 471.05 |
| 96.88 | 484.71 |
| 73.86 | 446.34 |
| 65.17 | 469.02 |
| 69.41 | 432.12 |
| 81.23 | 467.28 |
| 54.07 | 429.66 |
| 89.17 | 469.49 |
| 78.85 | 485.87 |
| 84.44 | 481.95 |
| 83.02 | 479.03 |
| 90.66 | 434.5 |
| 75.29 | 464.9 |
| 82.6 | 452.71 |
| 45.4 | 429.74 |
| 66.33 | 457.09 |
| 45.33 | 446.77 |
| 67.12 | 460.76 |
| 84.14 | 471.95 |
| 80.81 | 453.29 |
| 49.13 | 441.61 |
| 87.29 | 464.73 |
| 52.95 | 464.68 |
| 68.92 | 430.59 |
| 85.21 | 438.01 |
| 88.56 | 479.08 |
| 55.09 | 436.39 |
| 48.64 | 447.07 |
| 87.85 | 479.91 |
| 87.42 | 489.05 |
| 62.75 | 463.17 |
| 94.86 | 471.26 |
| 63.54 | 480.49 |
| 69.46 | 473.78 |
| 74.66 | 455.5 |
| 80.57 | 446.27 |
| 84.7 | 482.2 |
| 72.6 | 452.48 |
| 52.63 | 464.48 |
| 82.01 | 438.1 |
| 75.22 | 445.6 |
| 51.05 | 442.43 |
| 80.1 | 436.67 |
| 81.58 | 466.56 |
| 65.94 | 457.29 |
| 86.74 | 487.03 |
| 62.57 | 464.93 |
| 57.37 | 466.0 |
| 88.91 | 469.52 |
| 72.26 | 428.88 |
| 74.98 | 474.3 |
| 71.19 | 461.06 |
| 62.82 | 465.57 |
| 55.31 | 467.67 |
| 71.57 | 466.99 |
| 59.16 | 463.72 |
| 40.8 | 443.78 |
| 67.63 | 445.23 |
| 97.2 | 464.43 |
| 91.16 | 484.36 |
| 64.81 | 442.16 |
| 85.61 | 464.11 |
| 93.42 | 462.48 |
| 77.36 | 477.49 |
| 67.03 | 437.04 |
| 89.45 | 457.09 |
| 54.84 | 450.6 |
| 53.48 | 465.78 |
| 54.47 | 427.1 |
| 43.36 | 459.81 |
| 48.92 | 447.36 |
| 81.22 | 488.92 |
| 60.35 | 433.36 |
| 75.98 | 483.35 |
| 74.24 | 469.53 |
| 85.21 | 476.96 |
| 68.46 | 440.75 |
| 78.31 | 462.55 |
| 89.25 | 448.04 |
| 68.57 | 455.24 |
| 67.38 | 494.75 |
| 53.6 | 444.58 |
| 91.69 | 484.82 |
| 78.21 | 442.9 |
| 94.19 | 485.46 |
| 37.19 | 457.81 |
| 83.53 | 481.92 |
| 79.45 | 443.23 |
| 99.9 | 474.29 |
| 50.39 | 430.46 |
| 74.33 | 455.71 |
| 83.66 | 438.34 |
| 89.48 | 485.83 |
| 64.59 | 452.82 |
| 75.68 | 435.04 |
| 64.18 | 451.21 |
| 70.09 | 465.81 |
| 70.58 | 458.42 |
| 99.28 | 470.22 |
| 81.89 | 449.24 |
| 87.99 | 471.43 |
| 88.21 | 473.26 |
| 91.29 | 452.82 |
| 52.72 | 432.69 |
| 67.5 | 444.13 |
| 92.05 | 467.21 |
| 63.23 | 445.98 |
| 90.88 | 436.91 |
| 74.91 | 455.01 |
| 85.39 | 437.11 |
| 96.98 | 477.06 |
| 56.28 | 441.71 |
| 65.62 | 495.76 |
| 55.32 | 445.63 |
| 88.88 | 464.72 |
| 39.49 | 438.03 |
| 54.59 | 434.78 |
| 57.19 | 444.67 |
| 45.06 | 452.24 |
| 40.62 | 450.92 |
| 90.21 | 436.53 |
| 90.91 | 435.53 |
| 74.96 | 440.01 |
| 54.21 | 443.1 |
| 63.62 | 427.49 |
| 50.04 | 436.25 |
| 51.23 | 440.74 |
| 82.71 | 443.54 |
| 92.04 | 459.42 |
| 90.67 | 439.66 |
| 91.14 | 464.15 |
| 70.43 | 459.1 |
| 66.63 | 455.68 |
| 86.95 | 469.08 |
| 96.69 | 478.02 |
| 70.88 | 456.8 |
| 47.14 | 441.13 |
| 63.36 | 463.88 |
| 60.58 | 430.45 |
| 54.3 | 449.18 |
| 65.09 | 447.89 |
| 48.16 | 431.59 |
| 81.51 | 447.5 |
| 68.57 | 475.58 |
| 73.16 | 453.24 |
| 80.88 | 446.4 |
| 85.4 | 476.81 |
| 75.77 | 474.1 |
| 75.76 | 450.71 |
| 70.21 | 433.62 |
| 55.53 | 465.14 |
| 80.48 | 445.18 |
| 72.41 | 474.12 |
| 83.25 | 483.91 |
| 82.12 | 486.68 |
| 94.75 | 464.98 |
| 95.79 | 481.4 |
| 86.02 | 479.2 |
| 78.29 | 463.86 |
| 82.23 | 472.3 |
| 52.86 | 446.51 |
| 60.66 | 437.71 |
| 62.77 | 458.94 |
| 65.54 | 437.91 |
| 95.4 | 490.76 |
| 51.78 | 439.66 |
| 63.62 | 463.27 |
| 83.09 | 473.99 |
| 61.81 | 433.38 |
| 68.24 | 459.01 |
| 72.41 | 471.44 |
| 79.64 | 471.91 |
| 66.95 | 465.15 |
| 91.98 | 446.66 |
| 64.96 | 438.15 |
| 88.93 | 447.14 |
| 85.62 | 472.32 |
| 84.0 | 441.68 |
| 89.41 | 440.04 |
| 67.4 | 444.82 |
| 76.75 | 457.26 |
| 89.06 | 428.83 |
| 64.02 | 449.07 |
| 64.12 | 435.21 |
| 81.22 | 471.03 |
| 100.13 | 465.56 |
| 58.07 | 442.83 |
| 63.42 | 460.3 |
| 83.32 | 474.25 |
| 74.28 | 477.97 |
| 71.91 | 472.16 |
| 85.8 | 456.08 |
| 55.58 | 452.41 |
| 69.7 | 463.71 |
| 63.79 | 433.72 |
| 86.59 | 456.4 |
| 48.28 | 448.43 |
| 80.42 | 481.6 |
| 98.58 | 457.07 |
| 72.83 | 451.0 |
| 56.07 | 440.28 |
| 67.13 | 437.47 |
| 84.49 | 443.57 |
| 68.37 | 426.6 |
| 86.07 | 470.87 |
| 83.82 | 478.37 |
| 74.86 | 453.92 |
| 53.52 | 470.22 |
| 80.61 | 434.54 |
| 43.56 | 442.89 |
| 89.35 | 479.03 |
| 97.28 | 476.06 |
| 80.49 | 473.88 |
| 88.9 | 451.75 |
| 49.7 | 439.2 |
| 68.64 | 439.7 |
| 98.58 | 463.6 |
| 82.12 | 447.47 |
| 78.32 | 447.92 |
| 86.04 | 471.08 |
| 91.36 | 437.55 |
| 81.02 | 448.27 |
| 76.05 | 431.69 |
| 71.81 | 449.09 |
| 82.13 | 448.79 |
| 74.27 | 460.21 |
| 71.32 | 479.28 |
| 74.59 | 483.11 |
| 84.44 | 450.75 |
| 43.39 | 437.97 |
| 87.2 | 459.76 |
| 77.11 | 457.75 |
| 82.81 | 469.33 |
| 85.67 | 433.28 |
| 95.58 | 444.64 |
| 74.46 | 463.1 |
| 90.02 | 460.91 |
| 81.93 | 479.35 |
| 86.29 | 449.23 |
| 85.43 | 474.51 |
| 71.66 | 435.02 |
| 71.33 | 435.45 |
| 67.72 | 452.38 |
| 84.23 | 480.41 |
| 74.44 | 478.96 |
| 71.48 | 468.87 |
| 56.3 | 434.01 |
| 68.13 | 466.36 |
| 68.35 | 435.28 |
| 85.23 | 486.46 |
| 79.78 | 468.19 |
| 98.98 | 468.37 |
| 72.87 | 474.19 |
| 90.93 | 440.32 |
| 72.94 | 485.32 |
| 72.3 | 464.27 |
| 77.74 | 479.25 |
| 78.29 | 430.4 |
| 69.1 | 447.49 |
| 55.79 | 438.23 |
| 75.09 | 492.09 |
| 88.98 | 475.36 |
| 64.26 | 452.56 |
| 25.89 | 427.84 |
| 59.18 | 433.95 |
| 67.48 | 435.27 |
| 89.85 | 454.62 |
| 50.62 | 472.17 |
| 83.97 | 452.42 |
| 96.51 | 472.17 |
| 80.09 | 481.83 |
| 84.02 | 458.78 |
| 61.97 | 447.5 |
| 88.51 | 463.4 |
| 81.83 | 473.57 |
| 90.63 | 433.72 |
| 73.37 | 431.85 |
| 77.5 | 433.47 |
| 57.46 | 432.84 |
| 51.91 | 436.6 |
| 79.14 | 490.23 |
| 69.24 | 477.16 |
| 41.85 | 441.06 |
| 95.1 | 440.86 |
| 74.53 | 477.94 |
| 64.85 | 474.47 |
| 57.07 | 470.67 |
| 62.9 | 447.31 |
| 72.21 | 466.8 |
| 65.99 | 430.91 |
| 73.67 | 434.75 |
| 88.59 | 469.52 |
| 61.19 | 438.9 |
| 49.37 | 429.56 |
| 48.65 | 432.92 |
| 56.18 | 442.87 |
| 71.56 | 466.59 |
| 87.46 | 479.61 |
| 70.02 | 471.08 |
| 61.2 | 433.37 |
| 60.54 | 443.92 |
| 71.82 | 443.5 |
| 44.8 | 439.89 |
| 67.32 | 434.66 |
| 78.77 | 487.57 |
| 96.02 | 464.64 |
| 90.56 | 470.92 |
| 83.19 | 444.39 |
| 68.45 | 442.48 |
| 99.19 | 449.61 |
| 88.11 | 435.02 |
| 79.29 | 458.67 |
| 87.76 | 461.74 |
| 82.56 | 438.31 |
| 79.24 | 462.38 |
| 67.24 | 460.56 |
| 58.98 | 439.22 |
| 84.22 | 444.64 |
| 89.12 | 430.34 |
| 50.07 | 430.46 |
| 98.86 | 456.79 |
| 95.79 | 468.82 |
| 70.06 | 448.51 |
| 41.71 | 470.77 |
| 86.55 | 465.74 |
| 71.97 | 430.21 |
| 96.4 | 449.23 |
| 91.73 | 461.89 |
| 91.62 | 445.72 |
| 59.14 | 466.13 |
| 92.56 | 448.71 |
| 83.71 | 469.25 |
| 55.43 | 450.56 |
| 74.91 | 464.46 |
| 89.41 | 471.13 |
| 69.81 | 461.52 |
| 86.01 | 451.09 |
| 86.05 | 431.51 |
| 80.98 | 469.8 |
| 63.62 | 442.28 |
| 64.16 | 458.67 |
| 75.63 | 462.4 |
| 80.21 | 453.54 |
| 59.7 | 444.38 |
| 47.6 | 440.52 |
| 63.78 | 433.62 |
| 89.37 | 481.96 |
| 70.55 | 452.75 |
| 84.42 | 481.28 |
| 80.62 | 439.03 |
| 52.56 | 435.75 |
| 83.26 | 436.03 |
| 70.64 | 445.6 |
| 89.95 | 462.65 |
| 51.53 | 438.66 |
| 84.83 | 447.32 |
| 59.68 | 484.55 |
| 97.88 | 476.8 |
| 85.03 | 480.34 |
| 47.38 | 440.63 |
| 48.08 | 459.48 |
| 79.65 | 490.78 |
| 83.39 | 483.56 |
| 82.18 | 429.38 |
| 51.71 | 440.27 |
| 77.33 | 445.34 |
| 72.81 | 447.43 |
| 84.26 | 439.91 |
| 73.23 | 459.27 |
| 79.73 | 478.89 |
| 62.32 | 466.7 |
| 78.58 | 463.5 |
| 79.88 | 436.21 |
| 65.87 | 443.94 |
| 80.28 | 439.63 |
| 71.33 | 460.95 |
| 70.33 | 448.69 |
| 57.73 | 444.63 |
| 99.46 | 473.51 |
| 68.61 | 462.56 |
| 95.91 | 451.76 |
| 80.42 | 491.81 |
| 51.01 | 429.52 |
| 74.08 | 437.9 |
| 80.73 | 467.54 |
| 54.71 | 449.97 |
| 73.75 | 436.62 |
| 88.43 | 477.68 |
| 74.66 | 447.26 |
| 70.04 | 439.76 |
| 74.64 | 437.49 |
| 85.11 | 455.14 |
| 82.97 | 485.5 |
| 55.59 | 444.1 |
| 49.9 | 432.33 |
| 89.99 | 471.23 |
| 91.61 | 463.89 |
| 82.95 | 445.54 |
| 92.62 | 446.09 |
| 59.64 | 445.12 |
| 63.0 | 443.31 |
| 85.38 | 484.16 |
| 85.23 | 477.76 |
| 52.08 | 430.28 |
| 38.05 | 446.48 |
| 71.98 | 481.03 |
| 90.66 | 466.07 |
| 83.14 | 447.47 |
| 65.22 | 455.93 |
| 91.67 | 479.62 |
| 66.04 | 455.06 |
| 78.19 | 475.06 |
| 54.47 | 438.89 |
| 67.26 | 432.7 |
| 72.56 | 452.6 |
| 82.15 | 451.75 |
| 86.32 | 430.66 |
| 88.17 | 491.9 |
| 72.78 | 439.82 |
| 69.58 | 460.73 |
| 69.86 | 449.7 |
| 65.37 | 439.42 |
| 52.37 | 439.84 |
| 79.63 | 485.86 |
| 83.14 | 458.1 |
| 88.25 | 479.92 |
| 55.85 | 458.29 |
| 52.55 | 489.45 |
| 62.74 | 434.0 |
| 90.08 | 431.24 |
| 54.5 | 439.5 |
| 49.88 | 467.46 |
| 48.96 | 429.27 |
| 86.77 | 452.1 |
| 96.66 | 472.41 |
| 70.84 | 442.14 |
| 83.83 | 441.0 |
| 68.22 | 463.07 |
| 82.22 | 445.71 |
| 87.9 | 483.16 |
| 66.83 | 440.45 |
| 85.36 | 481.83 |
| 60.9 | 467.6 |
| 83.51 | 450.88 |
| 52.96 | 425.5 |
| 73.02 | 451.87 |
| 43.11 | 428.94 |
| 61.41 | 439.86 |
| 65.32 | 433.44 |
| 93.68 | 438.23 |
| 42.39 | 436.95 |
| 68.18 | 470.19 |
| 89.18 | 484.66 |
| 64.79 | 430.81 |
| 43.48 | 433.37 |
| 80.4 | 453.02 |
| 72.43 | 453.5 |
| 80.0 | 463.09 |
| 45.65 | 464.56 |
| 63.02 | 452.12 |
| 74.39 | 470.9 |
| 57.77 | 450.89 |
| 76.8 | 445.04 |
| 67.39 | 444.72 |
| 73.96 | 460.38 |
| 72.75 | 446.8 |
| 71.69 | 465.05 |
| 84.98 | 484.13 |
| 87.68 | 488.27 |
| 50.36 | 447.09 |
| 68.11 | 452.02 |
| 66.27 | 455.55 |
| 89.72 | 480.99 |
| 61.07 | 467.68 |
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
Linear Regression Model
- Linear Regression is one of the most useful work-horses of statistical learning
- See Chapter 7 of Kevin Murphy's Machine Learning froma Probabilistic Perspective for a good mathematical and algorithmic introduction.
- You should have already seen Ameet's treatment of the topic from earlier notebook.
Let's open http://spark.apache.org/docs/latest/mllib-linear-methods.html#regression for some details.
// First let's hold out 20% of our data for testing and leave 80% for training
var Array(split20, split80) = dataset.randomSplit(Array(0.20, 0.80), 1800009193L)
split20: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
split80: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
// Let's cache these datasets for performance
val testSet = split20.cache()
val trainingSet = split80.cache()
testSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
trainingSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
testSet.count() // action to actually cache
res87: Long = 1856
trainingSet.count() // action to actually cache
res88: Long = 7712
Let's take a few elements of the three DataFrames.
dataset.take(3)
res89: Array[org.apache.spark.sql.Row] = Array([14.96,41.76,1024.07,73.17,463.26], [25.18,62.96,1020.04,59.08,444.37], [5.11,39.4,1012.16,92.14,488.56])
testSet.take(3)
res90: Array[org.apache.spark.sql.Row] = Array([2.34,39.42,1028.47,69.68,490.34], [2.8,39.64,1011.01,82.96,482.66], [3.82,35.47,1016.62,84.34,489.04])
trainingSet.take(3)
res91: Array[org.apache.spark.sql.Row] = Array([1.81,39.42,1026.92,76.97,490.55], [2.58,39.42,1028.68,69.03,488.69], [2.64,39.64,1011.02,85.24,481.29])
// ***** LINEAR REGRESSION MODEL ****
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
// Let's initialize our linear regression learner
val lr = new LinearRegression()
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
lr: org.apache.spark.ml.regression.LinearRegression = linReg_3b8dffe4015f
// We use explain params to dump the parameters we can use
lr.explainParams()
res92: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
The cell below is based on the Spark ML pipeline API. More information can be found in the Spark ML Programming Guide at https://spark.apache.org/docs/latest/ml-guide.html
// Now we set the parameters for the method
lr.setPredictionCol("Predicted_PE")
.setLabelCol("PE")
.setMaxIter(100)
.setRegParam(0.1)
// We will use the new spark.ml pipeline API. If you have worked with scikit-learn this will be very familiar.
val lrPipeline = new Pipeline()
lrPipeline.setStages(Array(vectorizer, lr))
// Let's first train on the entire dataset to see what we get
val lrModel = lrPipeline.fit(trainingSet)
lrPipeline: org.apache.spark.ml.Pipeline = pipeline_07136143d91d
lrModel: org.apache.spark.ml.PipelineModel = pipeline_07136143d91d
Since Linear Regression is simply a line of best fit over the data that minimizes the square of the error, given multiple input dimensions we can express each predictor as a line function of the form:
\[ y = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_3 + \ldots + b_i x_i + \ldots + b_k x_k \]
where \(b_0\) is the intercept and \(b_i\)'s are coefficients.
To express the coefficients of that line we can retrieve the Estimator stage from the fitted, linear-regression pipeline model named lrModel and express the weights and the intercept for the function.
// The intercept is as follows:
val intercept = lrModel.stages(1).asInstanceOf[LinearRegressionModel].intercept
intercept: Double = 434.0183482461227
// The coefficents (i.e. weights) are as follows:
val weights = lrModel.stages(1).asInstanceOf[LinearRegressionModel].coefficients.toArray
weights: Array[Double] = Array(-1.9288465830313846, -0.24931659465920197, 0.08140785421485836, -0.1458797521995801)
The model has been fit and the intercept and coefficients displayed above.
Now, let us do some work to make a string of the model that is easy to understand for an applied data scientist or data analyst.
val featuresNoLabel = dataset.columns.filter(col => col != "PE")
featuresNoLabel: Array[String] = Array(AT, V, AP, RH)
val coefficentFeaturePairs = sc.parallelize(weights).zip(sc.parallelize(featuresNoLabel))
coefficentFeaturePairs: org.apache.spark.rdd.RDD[(Double, String)] = ZippedPartitionsRDD2[32671] at zip at command-2972105651606305:1
coefficentFeaturePairs.collect() // this just pairs each coefficient with the name of its corresponding feature
res93: Array[(Double, String)] = Array((-1.9288465830313846,AT), (-0.24931659465920197,V), (0.08140785421485836,AP), (-0.1458797521995801,RH))
// Now let's sort the coefficients from the largest to the smallest
var equation = s"y = $intercept "
//var variables = Array
coefficentFeaturePairs.sortByKey().collect().foreach({
case (weight, feature) =>
{
val symbol = if (weight > 0) "+" else "-"
val absWeight = Math.abs(weight)
equation += (s" $symbol (${absWeight} * ${feature})")
}
}
)
equation: String = y = 434.0183482461227 - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
// Finally here is our equation
println("Linear Regression Equation: " + equation)
Linear Regression Equation: y = 434.0183482461227 - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
Based on examining the fitted Linear Regression Equation above:
- There is a strong negative correlation between Atmospheric Temperature (AT) and Power Output due to the coefficient being greater than -1.91.
- But our other dimenensions seem to have little to no correlation with Power Output.
Do you remember Step 2: Explore Your Data? When we visualized each predictor against Power Output using a Scatter Plot, only the temperature variable seemed to have a linear correlation with Power Output so our final equation seems logical.
Now let's see what our predictions look like given this model.
val predictionsAndLabels = lrModel.transform(testSet)
display(predictionsAndLabels.select("AT", "V", "AP", "RH", "PE", "Predicted_PE"))
| AT | V | AP | RH | PE | Predicted_PE |
|---|---|---|---|---|---|
| 2.34 | 39.42 | 1028.47 | 69.68 | 490.34 | 493.23742177145215 |
| 2.8 | 39.64 | 1011.01 | 82.96 | 482.66 | 488.93663844863084 |
| 3.82 | 35.47 | 1016.62 | 84.34 | 489.04 | 488.2642491377776 |
| 3.98 | 35.47 | 1017.22 | 86.53 | 489.64 | 487.68500173970443 |
| 4.23 | 38.44 | 1016.46 | 76.64 | 489.0 | 487.8432005878593 |
| 4.32 | 35.47 | 1017.8 | 88.51 | 488.03 | 486.7875685475632 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 | 485.8682911927764 |
| 4.65 | 35.19 | 1018.23 | 94.78 | 489.36 | 485.34119715268844 |
| 4.78 | 42.85 | 1013.39 | 93.36 | 481.47 | 482.9938172155284 |
| 4.87 | 42.85 | 1012.69 | 94.72 | 482.05 | 482.5648390621137 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 | 487.00024243767876 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 | 483.3467525779819 |
| 5.01 | 39.4 | 1003.69 | 91.9 | 475.34 | 482.8336530053327 |
| 5.06 | 40.64 | 1021.49 | 93.7 | 483.73 | 483.6145343498689 |
| 5.15 | 35.19 | 1018.63 | 93.94 | 488.42 | 484.53187599470635 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 | 487.2657538698361 |
| 5.17 | 35.57 | 1026.68 | 79.86 | 491.32 | 487.10787889447494 |
| 5.21 | 41.31 | 1003.51 | 91.23 | 486.46 | 482.0547750131424 |
| 5.23 | 40.78 | 1025.13 | 92.74 | 483.12 | 483.688095258955 |
| 5.25 | 40.07 | 1019.48 | 67.7 | 495.23 | 487.0194077282659 |
| 5.28 | 45.87 | 1008.25 | 97.88 | 479.91 | 480.1986449575354 |
| 5.29 | 41.38 | 1020.62 | 83.83 | 492.12 | 484.35541367676683 |
| 5.42 | 41.38 | 1020.77 | 86.02 | 491.38 | 483.7973981417879 |
| 5.47 | 40.62 | 1018.66 | 83.61 | 481.56 | 484.07023605498495 |
| 5.51 | 41.03 | 1022.28 | 84.5 | 491.84 | 484.05572584065357 |
| 5.53 | 35.79 | 1011.19 | 94.01 | 484.64 | 483.0334383183464 |
| 5.54 | 41.38 | 1020.47 | 82.91 | 490.07 | 483.9952002249004 |
| 5.54 | 45.87 | 1008.69 | 95.91 | 478.02 | 480.02034741363497 |
| 5.56 | 45.87 | 1006.99 | 96.48 | 476.61 | 479.7602256710553 |
| 5.65 | 40.72 | 1022.46 | 85.17 | 487.09 | 483.7798894431585 |
| 5.7 | 40.62 | 1016.07 | 84.94 | 482.82 | 483.2217349280458 |
| 5.8 | 45.87 | 1009.14 | 92.06 | 481.6 | 480.1171178824119 |
| 5.82 | 40.78 | 1024.82 | 96.01 | 470.02 | 482.0478125504672 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 | 481.81327159305386 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 | 484.6333949755666 |
| 6.02 | 41.38 | 1021.2 | 88.71 | 490.57 | 482.2826790358646 |
| 6.03 | 41.17 | 1019.81 | 84.2 | 488.57 | 482.86050781997415 |
| 6.04 | 41.14 | 1027.8 | 86.4 | 480.39 | 483.17821215232124 |
| 6.04 | 41.14 | 1027.92 | 87.12 | 481.37 | 483.0829476732433 |
| 6.06 | 41.17 | 1019.67 | 84.7 | 489.62 | 482.71830544679335 |
| 6.13 | 40.64 | 1020.69 | 94.57 | 481.13 | 481.35862683823984 |
| 6.14 | 39.4 | 1011.21 | 90.87 | 485.94 | 481.4164995749685 |
| 6.15 | 40.77 | 1022.42 | 88.57 | 482.49 | 482.3037528502627 |
| 6.17 | 36.25 | 1028.68 | 90.59 | 483.77 | 483.6070229944035 |
| 6.22 | 39.33 | 1012.31 | 93.23 | 491.77 | 481.0249164343975 |
| 6.25 | 39.64 | 1010.98 | 83.45 | 483.43 | 482.2081944229683 |
| 6.3 | 41.14 | 1027.45 | 86.11 | 481.49 | 482.6905244198958 |
| 6.34 | 40.64 | 1020.62 | 94.39 | 478.78 | 480.9741288614041 |
| 6.38 | 40.07 | 1018.53 | 60.2 | 492.96 | 485.8565717694332 |
| 6.45 | 40.02 | 1032.08 | 79.7 | 481.36 | 483.99243959507345 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 | 481.76650494734884 |
| 6.49 | 43.65 | 1020.41 | 72.78 | 484.94 | 483.069724719673 |
| 6.52 | 39.85 | 1012.55 | 86.36 | 483.01 | 481.33834961288795 |
| 6.59 | 39.37 | 1020.34 | 77.92 | 488.17 | 483.1883946104104 |
| 6.65 | 39.33 | 1011.29 | 92.85 | 490.41 | 480.1679106982307 |
| 6.71 | 40.72 | 1022.78 | 80.69 | 483.11 | 482.4149038683481 |
| 6.72 | 38.91 | 1016.89 | 90.47 | 485.89 | 480.94068220101354 |
| 6.76 | 39.22 | 1013.91 | 77.0 | 487.08 | 482.508645049916 |
| 6.8 | 41.16 | 1023.17 | 95.4 | 477.8 | 480.01746628251317 |
| 6.84 | 41.06 | 1021.04 | 89.59 | 489.96 | 480.63940670945976 |
| 6.86 | 38.08 | 1019.62 | 77.37 | 486.92 | 483.0108446487773 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 | 482.57972730795177 |
| 6.91 | 39.37 | 1019.58 | 71.02 | 488.2 | 483.51586402481416 |
| 6.93 | 41.14 | 1027.18 | 84.67 | 479.06 | 481.6634377951154 |
| 6.99 | 35.19 | 1019.32 | 68.95 | 480.05 | 484.68450470880435 |
| 6.99 | 39.37 | 1020.19 | 75.06 | 487.18 | 482.8218608903564 |
| 7.05 | 43.65 | 1018.41 | 72.36 | 480.47 | 481.8880244206695 |
| 7.1 | 35.77 | 1015.39 | 92.1 | 480.36 | 480.6306788292839 |
| 7.11 | 41.74 | 1022.35 | 90.68 | 487.85 | 479.89671820679695 |
| 7.11 | 43.13 | 1018.96 | 87.82 | 486.11 | 479.6914116057231 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 | 481.1970697561745 |
| 7.3 | 43.65 | 1019.33 | 67.62 | 482.96 | 482.17217802621536 |
| 7.34 | 40.72 | 1023.01 | 80.08 | 483.92 | 481.3074409763495 |
| 7.4 | 41.04 | 1024.44 | 90.9 | 477.69 | 479.64992318380445 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 | 480.70714895561014 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 | 479.7024675196716 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 | 481.00544489343537 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 | 482.38901084355183 |
| 7.48 | 38.5 | 1014.01 | 77.35 | 488.43 | 481.25646633043965 |
| 7.52 | 41.66 | 1015.2 | 78.41 | 483.28 | 480.33371483717946 |
| 7.54 | 38.56 | 1016.49 | 69.1 | 486.76 | 482.5311759738776 |
| 7.55 | 41.04 | 1027.03 | 83.32 | 476.58 | 480.67721106043905 |
| 7.55 | 43.65 | 1019.09 | 61.71 | 481.61 | 482.53257783094546 |
| 7.57 | 41.14 | 1028.23 | 87.97 | 477.8 | 480.0330510466423 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 | 479.3324132109004 |
| 7.65 | 41.01 | 1024.31 | 97.17 | 475.89 | 478.2499419685471 |
| 7.67 | 41.66 | 1016.25 | 77.0 | 485.03 | 480.3355565472517 |
| 7.69 | 36.24 | 1013.08 | 88.37 | 482.46 | 479.7315598782737 |
| 7.7 | 40.35 | 1011.72 | 92.88 | 484.57 | 477.91894784424176 |
| 7.73 | 37.8 | 1020.71 | 63.93 | 483.94 | 483.45191519870116 |
| 7.73 | 39.04 | 1018.61 | 68.23 | 482.39 | 482.34452319301437 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 | 478.45760011663003 |
| 7.81 | 39.64 | 1011.42 | 86.68 | 482.22 | 478.7638216096892 |
| 7.82 | 40.72 | 1022.17 | 88.13 | 481.52 | 479.1388800137473 |
| 7.84 | 43.52 | 1022.23 | 96.51 | 483.79 | 477.1846287648614 |
| 7.87 | 42.85 | 1012.18 | 94.21 | 480.54 | 476.81117998099177 |
| 7.89 | 40.46 | 1019.61 | 74.53 | 477.27 | 480.84424359067077 |
| 7.9 | 40.0 | 1018.74 | 79.55 | 474.5 | 480.1364995691749 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 | 479.2235127059344 |
| 7.92 | 39.54 | 1011.51 | 84.41 | 481.44 | 478.91505388939413 |
| 7.95 | 41.26 | 1008.48 | 97.92 | 480.6 | 476.210862698602 |
| 7.97 | 38.5 | 1013.84 | 72.36 | 487.19 | 481.0254321330136 |
| 8.01 | 40.46 | 1019.42 | 76.15 | 475.42 | 480.36098930984286 |
| 8.01 | 40.62 | 1015.62 | 86.43 | 476.22 | 478.51210495606927 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 | 479.57731721621883 |
| 8.03 | 40.07 | 1016.06 | 47.46 | 488.02 | 484.33140555054337 |
| 8.04 | 40.64 | 1020.64 | 89.26 | 477.78 | 478.4450809561189 |
| 8.05 | 40.62 | 1018.16 | 73.67 | 477.2 | 480.5031526805204 |
| 8.07 | 43.41 | 1016.02 | 76.26 | 467.56 | 479.2169410835439 |
| 8.07 | 43.69 | 1017.05 | 87.34 | 485.18 | 477.6146348725092 |
| 8.11 | 41.92 | 1029.61 | 91.92 | 483.52 | 478.33312476559934 |
| 8.16 | 39.72 | 1020.54 | 82.11 | 480.21 | 479.4778900760471 |
| 8.18 | 40.02 | 1031.45 | 73.66 | 478.81 | 481.48536176155926 |
| 8.23 | 43.79 | 1016.11 | 82.11 | 484.67 | 477.9675154808001 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 | 479.6817134321857 |
| 8.25 | 41.26 | 1020.59 | 91.84 | 475.17 | 477.5050067316079 |
| 8.26 | 40.96 | 1025.23 | 89.22 | 485.73 | 478.32045063849523 |
| 8.27 | 39.64 | 1011.0 | 84.64 | 479.91 | 478.1399555772117 |
| 8.28 | 40.56 | 1023.29 | 79.44 | 486.4 | 479.65037308403333 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 | 477.1324329554068 |
| 8.3 | 36.3 | 1015.97 | 60.62 | 480.58 | 482.82343628916425 |
| 8.3 | 43.13 | 1020.02 | 83.11 | 484.07 | 478.16947013024355 |
| 8.33 | 38.08 | 1018.94 | 73.78 | 481.89 | 480.6437911412516 |
| 8.34 | 40.72 | 1023.62 | 83.75 | 483.14 | 478.8928744938167 |
| 8.35 | 43.52 | 1022.78 | 97.34 | 479.31 | 476.1246111330079 |
| 8.35 | 43.79 | 1016.2 | 85.23 | 484.21 | 477.288235770853 |
| 8.37 | 40.92 | 1021.82 | 86.03 | 476.02 | 478.30600580479216 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 | 478.2957350932866 |
| 8.42 | 42.28 | 1008.4 | 87.78 | 481.91 | 476.52270993699136 |
| 8.46 | 40.8 | 1023.57 | 81.27 | 485.06 | 478.99917896902446 |
| 8.48 | 38.5 | 1013.5 | 66.51 | 485.29 | 480.8674382556021 |
| 8.51 | 38.5 | 1013.33 | 64.28 | 482.39 | 481.1210453702997 |
| 8.61 | 43.8 | 1021.9 | 74.35 | 478.25 | 478.8354389662744 |
| 8.63 | 39.96 | 1024.39 | 99.47 | 475.79 | 476.29244393984663 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 | 479.0178844308566 |
| 8.67 | 40.77 | 1011.81 | 89.4 | 479.23 | 476.4582419334783 |
| 8.68 | 41.82 | 1032.83 | 73.62 | 478.61 | 480.1903466285615 |
| 8.72 | 36.25 | 1029.31 | 85.73 | 479.94 | 479.4487267515188 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 | 479.3384367489267 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 | 477.44189376811596 |
| 8.74 | 40.03 | 1016.81 | 93.37 | 481.07 | 476.33561360755584 |
| 8.75 | 36.3 | 1015.61 | 57.53 | 480.4 | 482.3769169335795 |
| 8.75 | 40.22 | 1008.96 | 90.53 | 482.8 | 476.04420182940044 |
| 8.76 | 41.82 | 1033.29 | 76.5 | 480.08 | 479.65335282852305 |
| 8.77 | 40.46 | 1019.68 | 77.84 | 475.0 | 478.6696951676176 |
| 8.8 | 42.32 | 1017.91 | 86.8 | 483.88 | 476.696926422392 |
| 8.81 | 43.56 | 1014.99 | 81.5 | 482.52 | 476.90393713153463 |
| 8.83 | 36.25 | 1028.86 | 85.6 | 478.45 | 479.2188844607746 |
| 8.85 | 40.22 | 1008.61 | 90.6 | 482.3 | 475.81261283946816 |
| 8.87 | 41.16 | 1023.17 | 94.13 | 472.45 | 476.2100211409317 |
| 8.88 | 36.66 | 1026.61 | 76.16 | 480.2 | 480.2141595165933 |
| 8.91 | 43.52 | 1022.78 | 98.0 | 478.38 | 474.9481764100585 |
| 8.93 | 40.46 | 1019.03 | 71.0 | 472.77 | 479.30598211413803 |
| 8.94 | 41.74 | 1022.55 | 90.74 | 483.53 | 476.3744577455605 |
| 8.96 | 40.02 | 1031.21 | 82.32 | 475.47 | 478.6980048877349 |
| 8.96 | 40.05 | 1015.91 | 89.4 | 467.24 | 476.41215657483474 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 | 480.7338755379764 |
| 9.01 | 38.56 | 1016.67 | 63.61 | 482.37 | 480.51130475015583 |
| 9.03 | 40.71 | 1025.98 | 81.94 | 484.97 | 478.0206284049 |
| 9.04 | 44.68 | 1023.14 | 90.73 | 479.53 | 475.4980717304681 |
| 9.06 | 43.79 | 1016.05 | 81.32 | 482.8 | 476.4769333498689 |
| 9.08 | 36.18 | 1020.24 | 68.37 | 477.26 | 480.5658974037096 |
| 9.08 | 36.71 | 1025.07 | 81.32 | 479.02 | 478.9378167534134 |
| 9.08 | 40.02 | 1031.2 | 75.34 | 476.69 | 479.483969889582 |
| 9.11 | 40.64 | 1020.68 | 86.91 | 476.62 | 476.72728884411293 |
| 9.12 | 41.49 | 1020.58 | 96.23 | 475.69 | 475.1283411969007 |
| 9.12 | 41.54 | 1018.61 | 79.26 | 482.95 | 477.43108128919135 |
| 9.13 | 39.04 | 1022.36 | 74.6 | 483.24 | 479.0201634085648 |
| 9.13 | 39.16 | 1014.14 | 86.17 | 484.86 | 476.63324412261045 |
| 9.14 | 37.36 | 1015.22 | 78.06 | 491.97 | 478.3337308000573 |
| 9.15 | 39.61 | 1018.11 | 75.29 | 474.88 | 478.39283560851754 |
| 9.15 | 39.61 | 1018.69 | 84.47 | 475.64 | 477.10087603877 |
| 9.16 | 41.03 | 1021.3 | 76.08 | 484.96 | 478.16396362897893 |
| 9.19 | 40.62 | 1017.78 | 68.91 | 475.42 | 478.96772021173297 |
| 9.2 | 40.03 | 1017.05 | 92.46 | 480.38 | 475.6006326388746 |
| 9.26 | 44.68 | 1023.22 | 91.44 | 478.82 | 474.9766634864767 |
| 9.29 | 39.04 | 1022.72 | 74.29 | 482.55 | 478.786077505979 |
| 9.3 | 38.18 | 1017.19 | 71.51 | 480.14 | 478.9365615888623 |
| 9.31 | 43.56 | 1015.09 | 79.96 | 482.55 | 476.1723094438278 |
| 9.32 | 37.73 | 1022.14 | 79.49 | 477.91 | 478.2490255806092 |
| 9.35 | 44.03 | 1011.02 | 88.11 | 476.25 | 474.4577268339357 |
| 9.37 | 40.11 | 1024.94 | 75.03 | 471.13 | 478.43777544278043 |
| 9.39 | 39.66 | 1019.22 | 75.33 | 482.16 | 478.00197412694763 |
| 9.41 | 34.69 | 1027.02 | 78.91 | 480.87 | 479.3152324207446 |
| 9.41 | 39.61 | 1016.14 | 78.38 | 477.34 | 477.2801935898294 |
| 9.45 | 39.04 | 1023.08 | 75.81 | 483.66 | 478.285031656868 |
| 9.46 | 42.28 | 1008.67 | 78.16 | 481.95 | 475.9420528274367 |
| 9.47 | 41.4 | 1026.49 | 87.9 | 479.68 | 476.17198214059135 |
| 9.48 | 44.68 | 1023.29 | 92.9 | 478.66 | 474.34503134979343 |
| 9.5 | 37.36 | 1015.13 | 63.41 | 478.8 | 479.7691576930105 |
| 9.51 | 43.79 | 1016.02 | 79.81 | 481.12 | 475.82678857769963 |
| 9.53 | 38.18 | 1018.43 | 75.33 | 476.54 | 478.0366119605891 |
| 9.53 | 38.38 | 1020.49 | 80.08 | 478.03 | 477.46151999839185 |
| 9.55 | 38.08 | 1019.34 | 68.38 | 479.23 | 479.110912113517 |
| 9.55 | 39.66 | 1018.8 | 74.88 | 480.74 | 477.72481326338215 |
| 9.59 | 34.69 | 1027.65 | 75.32 | 478.88 | 479.54303529435083 |
| 9.59 | 38.56 | 1017.52 | 61.89 | 481.05 | 479.71268358186353 |
| 9.61 | 44.03 | 1008.3 | 91.36 | 473.54 | 473.26068816423447 |
| 9.63 | 41.14 | 1027.99 | 87.89 | 469.73 | 476.05175958076205 |
| 9.68 | 38.16 | 1015.54 | 79.67 | 475.51 | 476.88388448180046 |
| 9.68 | 41.06 | 1022.75 | 87.44 | 476.67 | 475.61433131158714 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 | 477.7130066863276 |
| 9.72 | 41.44 | 1015.17 | 84.41 | 481.85 | 475.2673812565115 |
| 9.75 | 36.66 | 1026.21 | 70.12 | 479.45 | 479.3846135509556 |
| 9.76 | 39.16 | 1014.19 | 85.4 | 482.38 | 475.5344685772051 |
| 9.78 | 52.75 | 1022.97 | 78.31 | 469.58 | 473.85672752722735 |
| 9.82 | 39.64 | 1010.79 | 69.22 | 477.93 | 477.3826135030455 |
| 9.83 | 41.17 | 1019.34 | 72.29 | 478.21 | 477.2300569616709 |
| 9.84 | 36.66 | 1026.7 | 70.02 | 477.62 | 479.265495182268 |
| 9.86 | 37.83 | 1005.05 | 100.15 | 472.46 | 472.7773808573311 |
| 9.86 | 41.01 | 1018.49 | 98.71 | 467.37 | 473.2887424901299 |
| 9.87 | 40.81 | 1017.17 | 84.25 | 473.2 | 475.32128019247375 |
| 9.88 | 39.66 | 1017.81 | 76.05 | 480.05 | 476.83702080523557 |
| 9.92 | 40.64 | 1020.39 | 95.41 | 469.65 | 473.90133694043874 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 | 477.4312500724956 |
| 9.95 | 43.56 | 1014.85 | 82.62 | 477.88 | 474.53026960482526 |
| 9.96 | 41.26 | 1022.9 | 83.83 | 475.21 | 475.5632280329792 |
| 9.96 | 41.55 | 1002.59 | 65.86 | 475.91 | 476.45899184845075 |
| 9.96 | 42.03 | 1017.78 | 82.39 | 477.85 | 475.16451288467897 |
| 9.97 | 41.62 | 1013.99 | 96.02 | 464.86 | 472.95056743270436 |
| 9.98 | 41.01 | 1017.83 | 98.07 | 466.05 | 473.09691475779204 |
| 9.98 | 41.62 | 1013.56 | 95.77 | 463.16 | 472.93274352761154 |
| 9.99 | 41.82 | 1033.14 | 68.36 | 475.75 | 478.4561215361668 |
| 9.99 | 42.07 | 1018.78 | 85.68 | 471.6 | 474.6981382928799 |
| 10.01 | 40.78 | 1023.71 | 88.11 | 470.82 | 475.02803269176394 |
| 10.02 | 41.44 | 1017.37 | 77.65 | 479.63 | 475.85397168574394 |
| 10.06 | 34.69 | 1027.9 | 71.73 | 477.68 | 479.18053767427625 |
| 10.08 | 43.14 | 1010.67 | 85.9 | 478.73 | 473.5654621009553 |
| 10.09 | 37.14 | 1012.99 | 72.59 | 473.66 | 477.1725989266351 |
| 10.1 | 41.4 | 1024.29 | 85.94 | 474.28 | 475.0636358283201 |
| 10.1 | 41.58 | 1021.26 | 94.06 | 468.19 | 473.5875494551498 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 | 472.67682233352394 |
| 10.12 | 41.55 | 1005.78 | 62.34 | 475.46 | 476.9235641778536 |
| 10.12 | 43.72 | 1011.33 | 97.62 | 473.05 | 471.68772310073444 |
| 10.13 | 38.16 | 1013.57 | 79.04 | 474.92 | 475.9474342905188 |
| 10.15 | 41.46 | 1019.78 | 83.56 | 481.31 | 474.932278891215 |
| 10.15 | 43.41 | 1018.4 | 82.07 | 473.43 | 474.55112952359036 |
| 10.16 | 41.55 | 1005.69 | 58.04 | 477.27 | 477.46636654211125 |
| 10.18 | 43.5 | 1022.84 | 88.7 | 476.91 | 473.8650937482109 |
| 10.2 | 41.01 | 1021.39 | 96.64 | 468.27 | 473.17098851617544 |
| 10.2 | 41.46 | 1019.12 | 83.26 | 480.11 | 474.8258713039414 |
| 10.22 | 37.83 | 1005.94 | 93.53 | 471.79 | 473.1211730372522 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 | 474.62974157133897 |
| 10.24 | 39.28 | 1010.13 | 81.61 | 477.53 | 474.801072598715 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 | 471.6665106288944 |
| 10.25 | 41.46 | 1018.67 | 84.41 | 479.28 | 474.5250337253637 |
| 10.27 | 52.75 | 1026.19 | 76.78 | 470.76 | 473.3969220129792 |
| 10.28 | 39.64 | 1010.45 | 74.15 | 477.65 | 475.74847822607404 |
| 10.31 | 37.5 | 1008.55 | 99.31 | 474.16 | 472.3991408528041 |
| 10.31 | 38.18 | 1018.02 | 70.1 | 476.31 | 477.2616855096003 |
| 10.32 | 38.91 | 1012.11 | 81.49 | 479.17 | 474.9177051337058 |
| 10.33 | 40.62 | 1017.41 | 64.22 | 473.16 | 477.4228902388337 |
| 10.34 | 41.46 | 1017.75 | 89.73 | 478.03 | 473.50046202531144 |
| 10.37 | 37.83 | 1006.5 | 90.99 | 470.66 | 473.24796901874475 |
| 10.39 | 40.22 | 1006.59 | 87.77 | 479.14 | 473.09058493481064 |
| 10.4 | 42.44 | 1014.24 | 93.48 | 480.04 | 472.3076103285209 |
| 10.42 | 41.26 | 1008.48 | 96.76 | 472.54 | 471.615832151066 |
| 10.42 | 41.46 | 1021.04 | 89.16 | 479.24 | 473.69713759778955 |
| 10.44 | 37.83 | 1006.31 | 97.2 | 474.42 | 472.19156900447234 |
| 10.45 | 39.61 | 1020.23 | 73.39 | 477.41 | 476.3350912306915 |
| 10.46 | 37.5 | 1013.12 | 76.74 | 472.16 | 475.77435376625584 |
| 10.47 | 43.14 | 1010.35 | 86.86 | 476.55 | 472.6471168581127 |
| 10.51 | 37.5 | 1010.7 | 96.29 | 474.24 | 472.62895527440253 |
| 10.58 | 42.34 | 1022.32 | 94.01 | 474.91 | 472.5658087964315 |
| 10.59 | 38.38 | 1021.58 | 68.23 | 474.94 | 477.2343522450378 |
| 10.6 | 41.17 | 1018.38 | 87.92 | 478.47 | 473.38659302581107 |
| 10.6 | 41.46 | 1021.23 | 89.02 | 481.3 | 473.3858358704527 |
| 10.63 | 44.2 | 1018.63 | 90.26 | 477.19 | 472.2522916899094 |
| 10.66 | 41.93 | 1016.12 | 94.16 | 479.61 | 471.9871102146372 |
| 10.68 | 37.92 | 1009.59 | 65.05 | 474.03 | 476.66325912606675 |
| 10.7 | 44.77 | 1017.8 | 82.37 | 484.94 | 473.0585846959978 |
| 10.71 | 41.93 | 1018.23 | 90.88 | 478.76 | 472.5409240450936 |
| 10.73 | 40.35 | 1012.27 | 89.65 | 477.23 | 472.5905086170794 |
| 10.74 | 40.05 | 1015.45 | 87.33 | 477.93 | 473.2433331311532 |
| 10.76 | 44.58 | 1016.41 | 79.24 | 483.54 | 473.33367076102724 |
| 10.77 | 44.78 | 1012.87 | 84.16 | 470.66 | 472.25860679152254 |
| 10.82 | 39.96 | 1025.53 | 95.89 | 468.57 | 472.68332438968736 |
| 10.82 | 42.02 | 996.03 | 99.34 | 475.46 | 469.26491536026253 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 | 476.77045249286635 |
| 10.89 | 44.2 | 1018.3 | 86.32 | 479.16 | 472.2986932100967 |
| 10.91 | 37.92 | 1008.66 | 66.53 | 473.72 | 475.9280130742943 |
| 10.94 | 25.36 | 1009.47 | 100.1 | 470.9 | 474.1703211862971 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 | 473.8182300033406 |
| 10.94 | 40.81 | 1026.03 | 80.79 | 476.32 | 474.4834318795844 |
| 10.94 | 43.67 | 1012.36 | 73.3 | 477.34 | 473.75018039571677 |
| 10.98 | 37.5 | 1011.12 | 97.51 | 472.34 | 471.57861538146454 |
| 10.99 | 44.63 | 1020.67 | 90.09 | 473.29 | 471.6415723647869 |
| 11.01 | 43.69 | 1016.7 | 81.48 | 477.3 | 472.7701885173113 |
| 11.02 | 38.28 | 1013.51 | 95.66 | 472.11 | 471.77143688745184 |
| 11.02 | 40.0 | 1015.75 | 74.83 | 468.09 | 474.5636411763966 |
| 11.02 | 41.17 | 1018.18 | 86.86 | 477.62 | 472.7148284274264 |
| 11.04 | 39.96 | 1025.75 | 94.44 | 468.84 | 472.4884135100371 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 | 474.2579394355058 |
| 11.06 | 37.92 | 1008.76 | 67.32 | 473.16 | 475.5315818680234 |
| 11.06 | 41.16 | 1018.52 | 89.14 | 467.46 | 472.3352405654698 |
| 11.06 | 41.5 | 1013.09 | 89.8 | 476.24 | 471.7121476384473 |
| 11.1 | 40.92 | 1021.98 | 94.14 | 462.07 | 471.87019509945225 |
| 11.16 | 40.96 | 1023.49 | 83.7 | 478.3 | 473.39040211351204 |
| 11.17 | 39.72 | 1002.4 | 81.4 | 474.64 | 472.2988980097268 |
| 11.17 | 40.27 | 1009.54 | 74.56 | 476.18 | 473.7408434668035 |
| 11.18 | 37.5 | 1013.32 | 74.32 | 472.02 | 474.7548947976392 |
| 11.18 | 39.61 | 1018.56 | 68.0 | 468.75 | 475.5773739728955 |
| 11.2 | 41.38 | 1021.65 | 61.89 | 476.87 | 476.2403822241514 |
| 11.2 | 42.02 | 999.99 | 96.69 | 472.27 | 469.24091010473035 |
| 11.22 | 40.22 | 1011.01 | 81.67 | 476.7 | 472.7393314749417 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 | 472.63331852543564 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 | 471.38775711954423 |
| 11.31 | 39.61 | 1018.74 | 68.9 | 471.92 | 475.2099855538805 |
| 11.38 | 52.75 | 1026.19 | 72.71 | 469.9 | 471.84963289726664 |
| 11.41 | 39.61 | 1018.69 | 69.44 | 467.19 | 474.9342554366788 |
| 11.48 | 41.14 | 1027.67 | 90.5 | 464.07 | 472.0765967355643 |
| 11.49 | 35.76 | 1019.08 | 60.04 | 472.45 | 477.1428353332941 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 | 470.47813470324115 |
| 11.51 | 41.06 | 1021.3 | 78.11 | 476.91 | 473.32755876405025 |
| 11.53 | 41.14 | 1025.63 | 88.54 | 469.04 | 472.10000669812564 |
| 11.53 | 41.39 | 1018.39 | 85.52 | 474.49 | 471.88884153658796 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 | 471.68655893301997 |
| 11.54 | 40.77 | 1022.13 | 83.5 | 465.61 | 472.62327183365306 |
| 11.56 | 39.28 | 1011.27 | 82.05 | 477.71 | 472.2836129719507 |
| 11.56 | 41.62 | 1012.5 | 91.42 | 460.6 | 470.4334505230224 |
| 11.57 | 39.72 | 1002.26 | 78.69 | 474.72 | 471.91129640538503 |
| 11.57 | 41.54 | 1020.13 | 69.14 | 476.89 | 474.3054501914308 |
| 11.62 | 39.72 | 1018.4 | 68.06 | 471.56 | 474.6794786091428 |
| 11.67 | 37.73 | 1021.2 | 68.88 | 473.54 | 475.187496898361 |
| 11.67 | 44.6 | 1018.09 | 92.53 | 467.96 | 469.771457326924 |
| 11.68 | 40.55 | 1022.21 | 85.76 | 475.13 | 472.08490735121984 |
| 11.7 | 25.36 | 1008.65 | 92.11 | 470.88 | 473.8032225628117 |
| 11.71 | 41.44 | 1015.37 | 79.95 | 474.22 | 472.09588182193215 |
| 11.72 | 40.35 | 1012.08 | 83.98 | 476.41 | 471.4926212025492 |
| 11.76 | 41.58 | 1020.91 | 88.35 | 465.45 | 471.19014476340215 |
| 11.77 | 39.39 | 1012.9 | 85.8 | 478.51 | 471.43677609572336 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 | 469.7436046712689 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 | 471.72684171223557 |
| 11.8 | 41.54 | 1020.0 | 65.85 | 476.12 | 474.3311768410223 |
| 11.8 | 42.34 | 1020.25 | 93.54 | 466.52 | 470.11266519044227 |
| 11.8 | 43.99 | 1020.86 | 98.44 | 466.75 | 469.0361408145477 |
| 11.82 | 41.54 | 1019.96 | 67.65 | 476.97 | 474.02676004123384 |
| 11.82 | 41.67 | 1012.94 | 84.51 | 476.73 | 470.9633331252549 |
| 11.84 | 40.05 | 1014.54 | 86.78 | 473.82 | 471.12775460619287 |
| 11.86 | 39.85 | 1013.0 | 66.92 | 478.29 | 473.91084477665686 |
| 11.87 | 40.55 | 1019.06 | 94.11 | 473.79 | 470.2438958288006 |
| 11.9 | 39.16 | 1016.53 | 84.59 | 477.75 | 471.7153938676623 |
| 11.92 | 43.8 | 1022.96 | 60.49 | 470.33 | 474.55914246739445 |
| 11.93 | 38.78 | 1013.15 | 96.08 | 474.57 | 469.8009518761225 |
| 11.95 | 41.58 | 1015.83 | 89.35 | 464.57 | 470.2642322610151 |
| 11.96 | 43.41 | 1017.42 | 79.44 | 469.34 | 471.3638012594579 |
| 12.02 | 41.92 | 1030.1 | 84.45 | 465.82 | 471.92094622344274 |
| 12.02 | 43.69 | 1016.12 | 74.07 | 477.74 | 471.85580587680386 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 | 470.30107562853505 |
| 12.05 | 48.04 | 1009.14 | 100.13 | 464.14 | 466.34356012780466 |
| 12.05 | 49.83 | 1008.54 | 95.11 | 454.18 | 466.58075506687766 |
| 12.06 | 52.72 | 1024.53 | 80.05 | 467.21 | 469.3396022995035 |
| 12.07 | 38.25 | 1012.67 | 81.66 | 470.36 | 471.72756140636227 |
| 12.08 | 40.75 | 1015.84 | 86.9 | 476.84 | 470.5786344502193 |
| 12.09 | 40.6 | 1013.85 | 85.72 | 474.35 | 470.6068799512958 |
| 12.1 | 40.27 | 1005.53 | 68.37 | 477.61 | 472.5235663152981 |
| 12.1 | 41.17 | 1013.72 | 75.61 | 478.1 | 471.9097423001995 |
| 12.12 | 40.0 | 1018.72 | 84.42 | 462.1 | 471.2847044384862 |
| 12.14 | 40.83 | 1010.56 | 78.31 | 474.82 | 471.2662319288046 |
| 12.17 | 41.58 | 1013.89 | 88.98 | 463.03 | 469.73593028388524 |
| 12.19 | 40.05 | 1014.65 | 85.1 | 472.68 | 470.7066911497908 |
| 12.19 | 40.23 | 1017.45 | 82.07 | 474.91 | 471.33177180371854 |
| 12.19 | 44.63 | 1018.96 | 80.91 | 468.91 | 470.52692515963395 |
| 12.23 | 41.58 | 1018.76 | 87.66 | 464.45 | 470.2092170118331 |
| 12.24 | 49.83 | 1007.9 | 94.28 | 466.83 | 466.2832533837298 |
| 12.25 | 44.58 | 1016.47 | 81.15 | 475.24 | 470.18594349686214 |
| 12.27 | 41.17 | 1019.39 | 52.18 | 473.84 | 475.4613835085186 |
| 12.27 | 41.17 | 1019.41 | 58.1 | 475.13 | 474.5994035325814 |
| 12.3 | 39.85 | 1012.59 | 73.38 | 476.42 | 472.0863918606857 |
| 12.3 | 40.69 | 1015.74 | 82.58 | 474.46 | 470.7913069417126 |
| 12.31 | 44.03 | 1007.78 | 94.42 | 468.13 | 467.56407826412726 |
| 12.32 | 41.26 | 1022.42 | 79.74 | 470.27 | 471.5687225134983 |
| 12.32 | 43.69 | 1016.26 | 83.18 | 471.6 | 469.9595844589464 |
| 12.33 | 38.91 | 1017.24 | 79.84 | 472.49 | 471.6990473850642 |
| 12.33 | 39.85 | 1012.53 | 66.04 | 479.32 | 473.0943993730868 |
| 12.35 | 44.2 | 1017.81 | 82.49 | 471.65 | 470.00140680122996 |
| 12.36 | 40.56 | 1022.11 | 72.99 | 474.71 | 472.62554215897904 |
| 12.36 | 45.09 | 1013.05 | 88.4 | 467.2 | 468.51057584459073 |
| 12.36 | 48.04 | 1012.8 | 93.59 | 468.37 | 466.99762401287654 |
| 12.37 | 46.97 | 1013.95 | 90.76 | 464.4 | 467.7515630344035 |
| 12.38 | 45.09 | 1013.26 | 90.55 | 467.47 | 468.17545309508614 |
| 12.39 | 49.83 | 1007.6 | 92.43 | 468.43 | 466.2393815815799 |
| 12.42 | 41.58 | 1019.49 | 86.31 | 466.05 | 470.09910156010346 |
| 12.42 | 43.14 | 1015.88 | 79.48 | 471.1 | 470.4126440262426 |
| 12.43 | 43.22 | 1008.93 | 77.42 | 468.01 | 470.1081379355774 |
| 12.45 | 40.56 | 1017.84 | 66.52 | 477.41 | 473.04817642574005 |
| 12.47 | 38.25 | 1012.74 | 82.89 | 469.56 | 470.7822892277393 |
| 12.47 | 45.01 | 1017.8 | 95.25 | 467.18 | 467.70575905298347 |
| 12.49 | 41.62 | 1011.37 | 82.68 | 461.35 | 469.8226213597647 |
| 12.5 | 41.38 | 1021.87 | 57.59 | 474.4 | 474.3780743285961 |
| 12.5 | 43.67 | 1013.99 | 90.91 | 473.26 | 468.30493209232344 |
| 12.54 | 43.69 | 1017.26 | 83.59 | 470.04 | 469.5568353664925 |
| 12.55 | 39.58 | 1010.68 | 76.9 | 472.31 | 471.0025099661929 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 | 469.9361134525985 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 | 469.7928661275291 |
| 12.57 | 41.79 | 1014.99 | 87.8 | 461.88 | 469.1737219130261 |
| 12.58 | 39.72 | 1017.85 | 57.74 | 471.24 | 474.2884906123142 |
| 12.58 | 43.67 | 1014.36 | 91.72 | 473.02 | 468.06258267245875 |
| 12.59 | 39.18 | 1024.18 | 67.57 | 471.32 | 473.485146860658 |
| 12.6 | 41.74 | 1022.13 | 67.89 | 474.23 | 472.61404029065585 |
| 12.61 | 43.22 | 1013.41 | 78.94 | 466.85 | 469.903915514171 |
| 12.64 | 41.26 | 1020.79 | 83.66 | 465.78 | 470.2469481759357 |
| 12.65 | 44.34 | 1014.74 | 92.81 | 473.46 | 467.632447347929 |
| 12.68 | 41.4 | 1021.59 | 78.57 | 466.64 | 470.9425442114299 |
| 12.71 | 43.8 | 1023.15 | 71.16 | 466.2 | 471.494284203131 |
| 12.72 | 39.13 | 1008.36 | 92.59 | 467.28 | 468.30907898088435 |
| 12.72 | 40.64 | 1021.11 | 93.24 | 475.73 | 468.87573922525866 |
| 12.73 | 37.73 | 1021.89 | 61.76 | 470.89 | 474.237754775417 |
| 12.74 | 49.83 | 1007.44 | 91.47 | 466.09 | 465.69130458295615 |
| 12.75 | 39.85 | 1012.51 | 62.37 | 475.61 | 472.8180343417018 |
| 12.79 | 44.68 | 1022.51 | 99.55 | 465.75 | 466.9269506815448 |
| 12.83 | 41.67 | 1012.84 | 84.29 | 474.81 | 469.0391508364556 |
| 12.83 | 44.88 | 1017.86 | 87.88 | 474.26 | 468.1238036853617 |
| 12.87 | 39.3 | 1019.26 | 71.55 | 471.48 | 471.9340237695587 |
| 12.87 | 45.51 | 1015.3 | 86.67 | 475.77 | 467.85769076077656 |
| 12.88 | 44.0 | 1022.71 | 88.58 | 470.31 | 468.53947222591256 |
| 12.88 | 44.71 | 1018.73 | 51.95 | 469.12 | 473.38202950699997 |
| 12.89 | 36.71 | 1013.36 | 87.29 | 475.13 | 469.76472317857633 |
| 12.9 | 44.63 | 1020.72 | 89.51 | 467.41 | 468.04615604018346 |
| 12.92 | 39.35 | 1014.56 | 88.29 | 469.83 | 469.00047164404333 |
| 12.95 | 41.38 | 1021.97 | 53.83 | 474.46 | 474.0667420199239 |
| 12.99 | 40.55 | 1007.52 | 94.15 | 472.18 | 467.138305828078 |
| 13.02 | 45.51 | 1015.24 | 80.05 | 468.46 | 468.5292032616302 |
| 13.03 | 42.86 | 1014.39 | 86.25 | 475.03 | 468.1969526319267 |
| 13.04 | 40.69 | 1015.96 | 82.37 | 473.49 | 469.4125049461586 |
| 13.06 | 44.2 | 1018.95 | 85.68 | 469.02 | 468.259374271566 |
| 13.07 | 38.47 | 1015.16 | 57.26 | 468.9 | 473.50603668317063 |
| 13.07 | 40.83 | 1010.0 | 84.84 | 471.19 | 468.47422142636185 |
| 13.08 | 39.28 | 1012.41 | 77.98 | 474.13 | 470.0383017110002 |
| 13.08 | 44.9 | 1020.47 | 86.46 | 472.01 | 468.0562294553348 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 | 472.76694427363464 |
| 13.09 | 54.3 | 1017.61 | 82.38 | 471.0 | 466.0557279256278 |
| 13.1 | 40.55 | 1007.59 | 95.46 | 472.37 | 466.7407287783581 |
| 13.1 | 49.83 | 1007.29 | 92.79 | 466.08 | 464.79214736202914 |
| 13.11 | 41.44 | 1015.55 | 74.45 | 471.61 | 470.21248865654456 |
| 13.12 | 39.18 | 1023.11 | 64.33 | 471.44 | 472.8484021647681 |
| 13.15 | 40.78 | 1024.13 | 79.59 | 462.3 | 470.24854120855605 |
| 13.15 | 41.14 | 1026.72 | 80.31 | 461.49 | 470.2646001553115 |
| 13.18 | 43.72 | 1010.59 | 99.09 | 464.7 | 465.51076750880605 |
| 13.19 | 44.88 | 1017.61 | 94.95 | 467.68 | 466.3776971038656 |
| 13.2 | 40.83 | 1007.75 | 94.98 | 472.41 | 466.5610830112806 |
| 13.2 | 41.78 | 1010.49 | 64.96 | 468.58 | 470.92659992793443 |
| 13.25 | 43.7 | 1013.61 | 76.05 | 472.22 | 468.98765579029424 |
| 13.25 | 44.92 | 1024.11 | 89.34 | 469.18 | 467.5995301073336 |
| 13.27 | 40.27 | 1006.1 | 40.04 | 473.88 | 474.44599166986796 |
| 13.34 | 41.79 | 1011.48 | 86.66 | 461.12 | 467.5690713933053 |
| 13.41 | 40.89 | 1010.85 | 89.61 | 472.4 | 467.1768048505422 |
| 13.42 | 40.92 | 1022.84 | 75.89 | 458.49 | 470.12758725908657 |
| 13.43 | 43.69 | 1016.21 | 73.01 | 475.36 | 469.2980914389405 |
| 13.44 | 40.69 | 1014.54 | 77.97 | 473.0 | 469.1672380696391 |
| 13.44 | 41.14 | 1026.41 | 77.26 | 461.44 | 470.1249314556345 |
| 13.48 | 41.92 | 1030.2 | 65.96 | 463.9 | 471.81028761580865 |
| 13.49 | 43.41 | 1015.75 | 57.86 | 461.06 | 471.424799923348 |
| 13.51 | 39.31 | 1012.18 | 75.19 | 466.46 | 469.58969888462434 |
| 13.53 | 38.73 | 1004.86 | 85.38 | 472.46 | 467.6133054100996 |
| 13.53 | 42.86 | 1014.0 | 90.63 | 471.73 | 466.56182696263306 |
| 13.54 | 40.69 | 1015.85 | 77.55 | 471.05 | 469.14226719628124 |
| 13.55 | 40.71 | 1019.13 | 75.44 | 467.21 | 469.69281643752356 |
| 13.56 | 40.03 | 1017.73 | 83.76 | 472.59 | 468.5153727218602 |
| 13.56 | 49.83 | 1007.01 | 89.86 | 466.33 | 464.3095114085993 |
| 13.57 | 42.99 | 1007.51 | 88.95 | 472.04 | 466.169002951847 |
| 13.58 | 39.28 | 1016.17 | 79.17 | 472.17 | 469.2063750462149 |
| 13.61 | 38.47 | 1015.1 | 54.98 | 466.51 | 472.79218089209587 |
| 13.65 | 41.58 | 1020.56 | 72.17 | 460.8 | 469.8764663630868 |
| 13.67 | 41.48 | 1017.51 | 63.54 | 463.97 | 470.87346939701916 |
| 13.67 | 42.32 | 1015.41 | 79.04 | 464.56 | 468.2319508045607 |
| 13.69 | 34.03 | 1018.45 | 65.76 | 469.12 | 472.4449714286485 |
| 13.69 | 40.83 | 1008.53 | 84.81 | 471.26 | 467.1630433917525 |
| 13.71 | 43.41 | 1015.45 | 69.26 | 466.06 | 469.3130021437414 |
| 13.72 | 44.47 | 1027.2 | 68.89 | 470.66 | 470.0399558829108 |
| 13.72 | 49.83 | 1006.88 | 89.49 | 463.65 | 464.0442884425802 |
| 13.73 | 45.08 | 1023.55 | 81.64 | 470.35 | 467.7114787859095 |
| 13.74 | 42.74 | 1029.54 | 70.0 | 465.92 | 470.46126451393184 |
| 13.74 | 44.21 | 1023.26 | 84.2 | 466.67 | 467.51203531407947 |
| 13.77 | 42.86 | 1030.72 | 77.3 | 471.38 | 469.4046202019984 |
| 13.77 | 43.13 | 1016.63 | 62.13 | 468.45 | 470.40326389642064 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 | 469.66353144520883 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 | 465.52654943877593 |
| 13.8 | 39.82 | 1012.37 | 83.69 | 473.56 | 467.6786715108734 |
| 13.83 | 38.73 | 999.62 | 91.95 | 469.81 | 465.64964430715304 |
| 13.84 | 42.18 | 1015.74 | 79.76 | 468.66 | 467.8607823790049 |
| 13.85 | 45.08 | 1024.86 | 83.85 | 468.35 | 467.26426723260613 |
| 13.86 | 37.85 | 1011.4 | 89.7 | 469.94 | 467.0983914780623 |
| 13.86 | 40.66 | 1017.15 | 83.82 | 463.49 | 467.7236799517389 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 | 467.5049711098421 |
| 13.88 | 48.79 | 1017.28 | 79.4 | 464.14 | 466.31353063126903 |
| 13.9 | 39.54 | 1007.01 | 81.33 | 471.16 | 467.46352561567426 |
| 13.91 | 44.58 | 1021.36 | 78.98 | 472.67 | 467.6987016384138 |
| 13.93 | 42.86 | 1032.37 | 71.11 | 468.88 | 470.13332337428324 |
| 13.95 | 40.2 | 1013.18 | 90.77 | 464.79 | 466.32771593378925 |
| 13.95 | 71.14 | 1019.76 | 75.51 | 461.18 | 461.3756491943329 |
| 13.96 | 39.54 | 1011.05 | 85.72 | 468.82 | 467.03627043956425 |
| 13.97 | 45.01 | 1017.44 | 89.15 | 461.96 | 465.6730488393365 |
| 13.98 | 44.84 | 1023.6 | 89.09 | 462.81 | 466.20636936169376 |
| 14.0 | 41.78 | 1010.96 | 48.37 | 465.62 | 471.8419294419814 |
| 14.01 | 45.08 | 1023.28 | 82.49 | 464.79 | 467.025423832653 |
| 14.02 | 40.66 | 1017.05 | 84.14 | 465.39 | 467.36024219232854 |
| 14.03 | 44.88 | 1019.6 | 57.63 | 465.51 | 470.36369995609516 |
| 14.04 | 40.2 | 1013.29 | 89.54 | 465.25 | 466.34250670048556 |
| 14.04 | 40.83 | 1008.98 | 82.04 | 469.75 | 466.9286675356811 |
| 14.04 | 44.21 | 1021.93 | 86.12 | 468.64 | 466.5450197688411 |
| 14.07 | 40.78 | 1024.67 | 72.66 | 456.71 | 469.52890927618625 |
| 14.07 | 42.99 | 1007.57 | 96.05 | 468.87 | 464.17371789096717 |
| 14.07 | 43.34 | 1015.79 | 86.0 | 463.77 | 466.22172115408836 |
| 14.08 | 39.31 | 1011.67 | 72.0 | 464.16 | 468.9140947361635 |
| 14.09 | 41.2 | 1016.45 | 67.27 | 472.42 | 469.50273867747836 |
| 14.09 | 44.84 | 1023.65 | 94.29 | 466.12 | 465.2396919188332 |
| 14.1 | 41.04 | 1026.11 | 74.25 | 465.89 | 469.291500068156 |
| 14.1 | 42.18 | 1015.05 | 78.25 | 463.3 | 467.5233892738298 |
| 14.12 | 39.52 | 1016.79 | 75.89 | 472.32 | 468.6339203654876 |
| 14.12 | 41.39 | 1018.73 | 76.51 | 472.88 | 468.23518412428797 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 | 467.40072495010907 |
| 14.17 | 42.86 | 1030.94 | 66.47 | 466.2 | 470.2308690130346 |
| 14.18 | 39.3 | 1020.1 | 67.48 | 464.32 | 470.06934793478035 |
| 14.18 | 40.69 | 1014.73 | 74.88 | 471.52 | 468.2061275247934 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 | 466.5674959516581 |
| 14.22 | 42.32 | 1015.54 | 77.23 | 465.0 | 467.4457105564226 |
| 14.24 | 39.52 | 1018.22 | 77.19 | 468.51 | 468.3292283291916 |
| 14.24 | 39.99 | 1009.3 | 83.75 | 466.2 | 466.52892029567596 |
| 14.24 | 44.84 | 1023.6 | 94.06 | 466.67 | 464.97984688167367 |
| 14.27 | 41.48 | 1014.83 | 62.7 | 458.19 | 469.62052738975217 |
| 14.28 | 42.77 | 1020.06 | 81.77 | 466.75 | 466.9234567199092 |
| 14.28 | 43.02 | 1012.97 | 49.44 | 467.83 | 471.0002382734735 |
| 14.31 | 40.78 | 1024.3 | 76.41 | 463.54 | 468.4888161194508 |
| 14.32 | 45.08 | 1023.24 | 84.53 | 467.21 | 466.1266303832576 |
| 14.33 | 45.51 | 1015.42 | 71.55 | 468.92 | 467.25704554531416 |
| 14.34 | 42.86 | 1031.75 | 66.81 | 466.17 | 469.9193063400854 |
| 14.35 | 40.71 | 1023.09 | 69.5 | 473.56 | 469.33864000185486 |
| 14.35 | 49.83 | 1006.39 | 91.23 | 462.54 | 462.5353944778779 |
| 14.36 | 40.0 | 1016.16 | 68.79 | 460.42 | 469.0357845125853 |
| 14.38 | 40.66 | 1016.34 | 92.37 | 463.1 | 465.4074674853422 |
| 14.39 | 43.56 | 1012.97 | 59.17 | 469.35 | 469.2340241993221 |
| 14.4 | 40.2 | 1013.04 | 90.5 | 464.5 | 465.48772540492894 |
| 14.41 | 40.71 | 1016.78 | 69.77 | 467.01 | 468.6698381136833 |
| 14.41 | 40.83 | 1009.82 | 80.43 | 470.13 | 466.5182432985413 |
| 14.42 | 41.16 | 1004.32 | 88.42 | 467.25 | 464.80335793821706 |
| 14.43 | 35.85 | 1021.99 | 78.25 | 464.6 | 469.0300144538734 |
| 14.48 | 39.0 | 1016.75 | 75.97 | 464.56 | 468.0542535304745 |
| 14.48 | 40.89 | 1011.39 | 82.18 | 470.44 | 466.2407858068176 |
| 14.5 | 41.76 | 1023.94 | 84.42 | 464.23 | 466.68020136327283 |
| 14.52 | 40.35 | 1011.11 | 69.84 | 470.8 | 468.07562484757494 |
| 14.54 | 41.17 | 1015.15 | 67.78 | 470.19 | 468.4620083288529 |
| 14.54 | 43.14 | 1010.26 | 82.98 | 465.45 | 465.35539799683 |
| 14.55 | 44.84 | 1023.83 | 87.6 | 465.14 | 465.34301144661265 |
| 14.57 | 41.79 | 1007.61 | 82.85 | 457.21 | 465.4373435562456 |
| 14.58 | 41.92 | 1030.42 | 61.96 | 462.69 | 470.2899851111997 |
| 14.58 | 42.07 | 1017.9 | 86.14 | 460.66 | 465.705988879045 |
| 14.64 | 44.92 | 1025.54 | 91.12 | 462.64 | 464.775180629532 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 | 471.7241650123044 |
| 14.65 | 35.4 | 1016.16 | 60.26 | 469.61 | 470.86762962520095 |
| 14.65 | 41.92 | 1030.61 | 63.07 | 464.95 | 470.0085068177468 |
| 14.65 | 44.84 | 1023.39 | 87.76 | 467.18 | 465.090966572103 |
| 14.66 | 41.76 | 1022.12 | 78.13 | 463.6 | 467.14100725665213 |
| 14.66 | 42.07 | 1018.14 | 84.68 | 462.77 | 465.7842034756254 |
| 14.72 | 39.0 | 1016.42 | 76.42 | 464.93 | 467.49881987016624 |
| 14.74 | 43.71 | 1024.35 | 81.53 | 465.49 | 466.18608052784475 |
| 14.76 | 39.64 | 1010.37 | 81.99 | 465.82 | 465.9570356485115 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 | 465.6600911572598 |
| 14.77 | 58.2 | 1018.78 | 83.83 | 460.54 | 461.72665249570616 |
| 14.78 | 38.58 | 1017.02 | 82.4 | 460.54 | 466.6642858393167 |
| 14.79 | 47.83 | 1007.27 | 92.04 | 463.22 | 462.1388114830899 |
| 14.81 | 39.58 | 1011.62 | 73.64 | 467.32 | 467.1954080636746 |
| 14.81 | 40.71 | 1018.54 | 73.0 | 467.66 | 467.57038570428426 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 | 466.8779893764293 |
| 14.82 | 42.74 | 1028.04 | 69.81 | 466.34 | 468.28371557585353 |
| 14.83 | 53.82 | 1016.57 | 63.47 | 464.0 | 465.49312878230023 |
| 14.84 | 71.14 | 1019.61 | 66.78 | 454.16 | 460.9202947940051 |
| 14.85 | 45.01 | 1013.12 | 85.53 | 460.19 | 464.1520666190232 |
| 14.86 | 37.85 | 1010.58 | 86.8 | 467.71 | 465.5258417359535 |
| 14.87 | 41.23 | 997.39 | 82.06 | 465.01 | 464.28156360850716 |
| 14.87 | 54.3 | 1017.17 | 71.57 | 462.87 | 464.1635216732549 |
| 14.88 | 42.28 | 1007.26 | 71.3 | 466.73 | 466.3736543730528 |
| 14.91 | 39.28 | 1014.57 | 75.23 | 469.34 | 467.08552274770574 |
| 14.91 | 40.73 | 1017.44 | 86.91 | 458.99 | 465.25377872135545 |
| 14.92 | 41.16 | 1004.83 | 83.7 | 465.72 | 464.56900508273293 |
| 14.92 | 46.18 | 1014.21 | 98.82 | 465.63 | 461.87533959682145 |
| 14.93 | 42.44 | 1012.65 | 86.8 | 471.24 | 464.4149735638803 |
| 14.94 | 40.0 | 1017.69 | 65.43 | 456.41 | 468.5317634787664 |
| 14.94 | 42.77 | 1018.06 | 75.35 | 460.51 | 466.42415027580006 |
| 14.98 | 39.58 | 1011.78 | 75.07 | 467.77 | 466.6719213555882 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 | 464.60786745115865 |
| 15.01 | 39.52 | 1017.53 | 72.0 | 468.02 | 467.544960954765 |
| 15.02 | 37.64 | 1016.49 | 83.28 | 463.93 | 466.26419991369926 |
| 15.03 | 43.71 | 1025.07 | 83.25 | 463.12 | 465.43441550001705 |
| 15.03 | 44.45 | 1020.94 | 65.57 | 460.06 | 467.4928608009505 |
| 15.08 | 42.77 | 1018.67 | 73.89 | 461.6 | 466.41675498345813 |
| 15.09 | 41.76 | 1022.4 | 76.22 | 463.27 | 466.61302775183003 |
| 15.11 | 43.67 | 1012.06 | 91.75 | 467.82 | 462.99098636012917 |
| 15.12 | 48.92 | 1011.8 | 72.93 | 462.59 | 464.3870766666383 |
| 15.14 | 39.72 | 1002.96 | 72.52 | 460.15 | 465.9823776729848 |
| 15.14 | 44.21 | 1019.97 | 83.86 | 463.1 | 464.5934173732165 |
| 15.15 | 53.82 | 1016.34 | 62.59 | 461.6 | 464.9855482511964 |
| 15.19 | 42.03 | 1017.38 | 71.66 | 462.64 | 466.6093718548404 |
| 15.21 | 50.88 | 1014.24 | 100.11 | 460.56 | 459.9584434481331 |
| 15.24 | 48.6 | 1007.08 | 86.49 | 459.85 | 461.87302187524506 |
| 15.27 | 38.73 | 1002.83 | 77.77 | 465.99 | 465.2019993258076 |
| 15.27 | 39.54 | 1010.64 | 81.91 | 464.49 | 465.03190605144545 |
| 15.29 | 38.73 | 1000.9 | 81.17 | 468.62 | 464.51031407803373 |
| 15.31 | 40.66 | 1016.46 | 84.64 | 458.26 | 464.7510595901315 |
| 15.31 | 41.35 | 1005.09 | 95.25 | 466.5 | 462.10563966655616 |
| 15.32 | 45.01 | 1013.3 | 83.72 | 459.31 | 463.52420449023833 |
| 15.34 | 43.5 | 1021.18 | 79.44 | 459.77 | 465.12795484714036 |
| 15.34 | 71.14 | 1019.79 | 77.56 | 457.1 | 458.39794118753656 |
| 15.4 | 38.73 | 1000.67 | 79.71 | 469.18 | 464.49240158564226 |
| 15.41 | 42.44 | 1012.6 | 86.74 | 472.28 | 463.4938095964465 |
| 15.46 | 42.07 | 1017.9 | 81.12 | 459.15 | 464.74092024201923 |
| 15.47 | 43.13 | 1015.11 | 50.5 | 466.63 | 468.69706628494185 |
| 15.48 | 53.82 | 1016.1 | 64.77 | 462.69 | 464.01147313398934 |
| 15.5 | 44.34 | 1019.21 | 65.21 | 468.53 | 466.52540885533836 |
| 15.5 | 49.25 | 1021.41 | 77.92 | 453.99 | 463.6262300043777 |
| 15.52 | 41.93 | 1022.97 | 52.92 | 471.97 | 469.18664060318713 |
| 15.52 | 58.59 | 1014.12 | 91.22 | 457.74 | 458.7253721171194 |
| 15.55 | 39.63 | 1004.98 | 89.82 | 466.83 | 462.85471321992253 |
| 15.55 | 43.02 | 1011.97 | 44.66 | 466.2 | 469.16650047432273 |
| 15.55 | 43.71 | 1024.34 | 79.61 | 465.14 | 464.90298984127037 |
| 15.55 | 45.09 | 1014.33 | 62.19 | 457.36 | 466.2852656032666 |
| 15.61 | 38.52 | 1018.4 | 80.99 | 439.21 | 465.39633546049805 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 | 462.31339644999167 |
| 15.62 | 58.59 | 1013.91 | 97.6 | 457.3 | 457.58467899039783 |
| 15.66 | 41.35 | 1001.68 | 86.26 | 463.57 | 462.46440155189674 |
| 15.67 | 45.17 | 1018.73 | 94.74 | 462.09 | 461.6436673101792 |
| 15.69 | 37.87 | 1021.18 | 84.38 | 465.41 | 465.13586499514474 |
| 15.69 | 39.3 | 1019.03 | 60.57 | 464.17 | 468.0777122780922 |
| 15.7 | 42.99 | 1007.51 | 76.05 | 464.59 | 463.94240853336476 |
| 15.75 | 36.99 | 1007.67 | 93.8 | 464.38 | 462.7655254273002 |
| 15.75 | 39.99 | 1007.02 | 77.44 | 464.95 | 464.3512532840681 |
| 15.79 | 58.86 | 1015.85 | 91.91 | 455.15 | 458.1774466179169 |
| 15.81 | 58.59 | 1014.41 | 90.03 | 456.91 | 458.36321179088014 |
| 15.84 | 41.04 | 1025.64 | 63.43 | 456.21 | 467.4754642409999 |
| 15.85 | 42.28 | 1007.4 | 82.12 | 467.3 | 462.93565136830296 |
| 15.85 | 49.69 | 1015.48 | 88.65 | 464.72 | 460.79339608207107 |
| 15.86 | 38.62 | 1016.65 | 67.51 | 462.58 | 466.71318747004864 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 | 469.2173130099923 |
| 15.86 | 43.5 | 1021.22 | 75.38 | 459.42 | 464.720482732063 |
| 15.92 | 37.64 | 1014.93 | 83.73 | 464.14 | 464.335595847906 |
| 15.92 | 39.16 | 1006.59 | 71.32 | 460.45 | 465.0880608446689 |
| 15.92 | 41.2 | 1016.04 | 73.37 | 468.91 | 465.0497057218854 |
| 15.96 | 41.66 | 1011.93 | 55.47 | 466.39 | 467.13452750857033 |
| 15.98 | 39.64 | 1009.31 | 81.21 | 467.22 | 463.63133669846115 |
| 15.98 | 44.68 | 1018.48 | 85.94 | 462.77 | 462.43127985662505 |
| 15.99 | 39.63 | 1006.09 | 89.92 | 464.95 | 462.0817954663473 |
| 16.0 | 40.66 | 1016.12 | 89.23 | 457.12 | 462.7228887148107 |
| 16.0 | 44.9 | 1020.5 | 80.89 | 461.5 | 463.2389898882613 |
| 16.0 | 45.09 | 1014.31 | 60.02 | 458.46 | 465.7322155460913 |
| 16.01 | 43.69 | 1017.12 | 62.43 | 465.89 | 465.9391561803266 |
| 16.02 | 39.54 | 1007.73 | 72.81 | 466.15 | 464.67588000342283 |
| 16.02 | 44.9 | 1009.3 | 82.62 | 455.48 | 462.03627301808893 |
| 16.09 | 44.71 | 1017.86 | 42.74 | 464.95 | 468.46315966006046 |
| 16.09 | 65.46 | 1013.84 | 85.52 | 454.88 | 456.7218449478403 |
| 16.12 | 45.87 | 1008.15 | 86.12 | 457.41 | 460.9973530979208 |
| 16.14 | 44.71 | 1014.83 | 39.41 | 468.88 | 468.60583110746245 |
| 16.16 | 25.88 | 1009.58 | 72.24 | 461.86 | 468.0452621538944 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 | 461.16748971043216 |
| 16.19 | 36.99 | 1007.37 | 92.4 | 462.94 | 462.09664222758136 |
| 16.2 | 45.76 | 1014.73 | 89.84 | 460.87 | 460.8634611992421 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 | 464.39198726436115 |
| 16.22 | 50.88 | 1014.33 | 100.09 | 454.94 | 458.02055270119473 |
| 16.23 | 43.69 | 1016.4 | 68.9 | 466.22 | 464.5123542802937 |
| 16.29 | 53.82 | 1014.97 | 73.15 | 459.95 | 461.1346442030387 |
| 16.3 | 41.16 | 1007.88 | 76.61 | 463.47 | 463.18977819659995 |
| 16.31 | 52.75 | 1024.4 | 55.69 | 456.58 | 464.6775725663142 |
| 16.32 | 43.56 | 1014.4 | 59.77 | 463.57 | 465.5402356742791 |
| 16.33 | 42.44 | 1013.98 | 84.9 | 462.44 | 462.1000323229214 |
| 16.34 | 47.45 | 1009.41 | 92.96 | 448.59 | 459.28384302135794 |
| 16.36 | 39.99 | 1008.91 | 72.48 | 462.5 | 464.0520812837949 |
| 16.37 | 36.99 | 1006.37 | 90.11 | 463.76 | 462.0021066209579 |
| 16.39 | 52.75 | 1024.42 | 54.61 | 459.48 | 464.6824431291315 |
| 16.39 | 58.59 | 1014.58 | 90.34 | 455.05 | 457.21309738475657 |
| 16.4 | 44.9 | 1009.22 | 82.31 | 456.11 | 461.3420214113817 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 | 467.91529134370023 |
| 16.47 | 38.01 | 1022.3 | 72.29 | 461.54 | 465.4513233379416 |
| 16.47 | 44.89 | 1010.04 | 82.01 | 459.69 | 461.3200136826322 |
| 16.49 | 49.39 | 1018.1 | 93.13 | 461.54 | 459.19347653551756 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 | 459.1522349051031 |
| 16.51 | 51.86 | 1022.37 | 81.18 | 442.48 | 460.6299621913311 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 | 465.94867946942674 |
| 16.56 | 42.99 | 1007.48 | 74.45 | 464.62 | 462.5145658398506 |
| 16.57 | 43.7 | 1015.67 | 71.95 | 465.78 | 463.3496922983309 |
| 16.57 | 53.82 | 1015.17 | 63.69 | 462.52 | 461.99087118644087 |
| 16.57 | 63.31 | 1016.19 | 81.02 | 454.78 | 457.1797966088055 |
| 16.59 | 43.56 | 1012.88 | 59.61 | 465.03 | 464.9190479188059 |
| 16.62 | 39.99 | 1007.07 | 77.14 | 463.74 | 462.72099107520137 |
| 16.62 | 54.3 | 1017.99 | 63.76 | 459.59 | 461.9941154580848 |
| 16.65 | 46.18 | 1010.6 | 95.69 | 465.6 | 458.7011562788462 |
| 16.7 | 36.99 | 1006.19 | 89.33 | 464.7 | 461.4647200415145 |
| 16.73 | 54.3 | 1017.96 | 59.44 | 460.54 | 462.40970062782714 |
| 16.77 | 42.28 | 1007.53 | 73.19 | 465.52 | 462.47440172010425 |
| 16.82 | 41.66 | 1010.49 | 63.14 | 465.64 | 464.23959443772316 |
| 16.82 | 45.0 | 1022.05 | 37.28 | 468.22 | 468.12040219816635 |
| 16.85 | 39.64 | 1008.82 | 80.81 | 464.2 | 461.97170222353844 |
| 16.85 | 42.24 | 1017.43 | 66.01 | 472.63 | 464.18342103476823 |
| 16.87 | 52.05 | 1012.7 | 71.63 | 460.31 | 460.4941449517029 |
| 16.89 | 49.21 | 1015.19 | 70.39 | 458.25 | 461.5472235985969 |
| 16.94 | 49.64 | 1024.43 | 69.22 | 459.25 | 462.26646301676067 |
| 16.97 | 42.86 | 1013.92 | 74.8 | 463.62 | 462.2293585659873 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 | 464.22591401634116 |
| 17.02 | 51.86 | 1021.53 | 81.28 | 460.0 | 459.5632798612247 |
| 17.03 | 43.99 | 1021.5 | 82.32 | 460.25 | 461.3519558174483 |
| 17.07 | 41.35 | 1005.88 | 83.43 | 464.6 | 460.4994805562497 |
| 17.08 | 38.58 | 1015.41 | 73.42 | 461.49 | 463.40687222781077 |
| 17.08 | 58.86 | 1016.04 | 87.46 | 449.98 | 456.35386691539543 |
| 17.19 | 43.14 | 1014.34 | 68.62 | 464.72 | 462.67093183857946 |
| 17.27 | 43.52 | 1021.37 | 76.73 | 460.08 | 461.81109623075827 |
| 17.27 | 44.9 | 1007.85 | 78.8 | 454.19 | 460.0644340540906 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 | 462.014274652855 |
| 17.3 | 43.72 | 1009.64 | 77.86 | 456.55 | 460.5836092644097 |
| 17.32 | 43.7 | 1015.13 | 61.66 | 464.5 | 463.360199769915 |
| 17.32 | 44.34 | 1019.52 | 56.24 | 468.8 | 464.34868588625807 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 | 461.6467454037935 |
| 17.35 | 52.72 | 1026.31 | 58.01 | 463.65 | 462.4960995942487 |
| 17.36 | 43.96 | 1013.02 | 79.59 | 466.36 | 460.43082906265056 |
| 17.37 | 48.92 | 1011.91 | 58.4 | 455.53 | 462.1757595182412 |
| 17.39 | 46.21 | 1013.94 | 82.73 | 454.06 | 459.4288341311474 |
| 17.4 | 63.09 | 1020.84 | 92.58 | 453.0 | 454.3258801823864 |
| 17.41 | 40.55 | 1003.91 | 76.87 | 461.47 | 460.83972369537236 |
| 17.44 | 44.89 | 1009.9 | 80.54 | 457.67 | 459.652078633235 |
| 17.45 | 50.9 | 1012.16 | 83.8 | 458.67 | 457.84281119185783 |
| 17.46 | 39.99 | 1008.52 | 69.73 | 461.01 | 462.29977029786545 |
| 17.48 | 52.9 | 1020.03 | 76.47 | 458.34 | 458.99629100134234 |
| 17.61 | 56.53 | 1019.5 | 82.3 | 457.01 | 456.94689658887796 |
| 17.64 | 57.76 | 1016.28 | 85.04 | 455.75 | 455.92052796835753 |
| 17.66 | 60.08 | 1017.22 | 86.96 | 456.62 | 455.0999707958263 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 | 463.6885973525347 |
| 17.67 | 50.88 | 1015.64 | 90.55 | 456.16 | 456.72206228080466 |
| 17.7 | 49.21 | 1015.16 | 67.91 | 455.97 | 460.34419741616995 |
| 17.74 | 49.69 | 1006.09 | 80.7 | 457.05 | 457.5432003190509 |
| 17.74 | 50.88 | 1015.56 | 89.78 | 457.71 | 456.69285780084897 |
| 17.75 | 55.5 | 1020.15 | 81.26 | 459.43 | 457.13828420727975 |
| 17.77 | 52.9 | 1020.11 | 81.51 | 457.98 | 457.7082041695146 |
| 17.79 | 40.12 | 1012.74 | 79.03 | 459.13 | 460.61769921749 |
| 17.82 | 49.15 | 1020.73 | 70.25 | 457.35 | 460.2397779497155 |
| 17.83 | 66.86 | 1011.65 | 77.31 | 456.56 | 454.03599822567077 |
| 17.84 | 61.27 | 1020.1 | 70.68 | 454.57 | 457.06546864918414 |
| 17.85 | 48.14 | 1017.16 | 86.68 | 451.9 | 457.7462919446442 |
| 17.86 | 50.88 | 1015.59 | 88.28 | 457.33 | 456.68265807481106 |
| 17.89 | 42.42 | 1008.92 | 65.08 | 467.59 | 461.5754309315541 |
| 17.89 | 44.6 | 1014.48 | 42.51 | 463.99 | 464.77705443177615 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 | 459.8014970847607 |
| 17.9 | 58.33 | 1013.6 | 85.81 | 452.28 | 454.94641693932414 |
| 17.94 | 62.1 | 1019.81 | 82.65 | 453.55 | 454.8958623057626 |
| 17.97 | 65.94 | 1012.92 | 88.22 | 448.88 | 452.5071708494883 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 | 455.24182428555105 |
| 18.0 | 44.06 | 1016.8 | 78.88 | 454.59 | 459.58273190303845 |
| 18.01 | 62.26 | 1011.89 | 89.29 | 451.14 | 453.10756062981807 |
| 18.02 | 53.16 | 1013.41 | 82.84 | 458.01 | 456.42171751548034 |
| 18.03 | 53.16 | 1013.02 | 81.95 | 456.55 | 456.5005129659639 |
| 18.03 | 53.16 | 1013.06 | 82.03 | 458.04 | 456.4920988999565 |
| 18.04 | 44.85 | 1015.13 | 48.4 | 463.31 | 463.61908166044077 |
| 18.06 | 65.48 | 1018.79 | 77.95 | 454.34 | 454.4243094498896 |
| 18.11 | 58.95 | 1016.61 | 89.17 | 454.29 | 454.14166454199494 |
| 18.13 | 60.1 | 1009.67 | 84.75 | 455.82 | 453.8961915229473 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 | 453.6649941349345 |
| 18.14 | 67.71 | 1003.82 | 95.69 | 442.45 | 449.9074433355401 |
| 18.16 | 43.72 | 1008.64 | 75.22 | 454.98 | 459.22851589459475 |
| 18.16 | 43.96 | 1012.78 | 78.33 | 465.26 | 459.05202239898534 |
| 18.17 | 52.08 | 1001.91 | 100.09 | 451.06 | 452.94903640134396 |
| 18.17 | 66.86 | 1011.29 | 78.48 | 452.77 | 453.1802042498492 |
| 18.2 | 52.72 | 1025.87 | 54.26 | 463.47 | 461.3678096135659 |
| 18.21 | 39.54 | 1009.81 | 70.92 | 461.73 | 460.89674705500823 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 | 463.8188758742396 |
| 18.22 | 45.09 | 1013.62 | 75.56 | 454.74 | 459.1270333631719 |
| 18.22 | 58.2 | 1017.09 | 81.92 | 451.04 | 455.213182837326 |
| 18.24 | 49.5 | 1014.37 | 79.36 | 464.13 | 457.4956830813669 |
| 18.25 | 60.1 | 1009.72 | 90.15 | 456.25 | 452.8810496638165 |
| 18.26 | 58.96 | 1014.04 | 69.7 | 457.43 | 456.48090497858726 |
| 18.27 | 58.2 | 1018.34 | 72.73 | 448.17 | 456.5591352486571 |
| 18.28 | 60.1 | 1009.72 | 85.79 | 452.93 | 453.45921998591575 |
| 18.31 | 62.1 | 1020.38 | 79.02 | 455.24 | 454.7581350474279 |
| 18.32 | 39.53 | 1008.15 | 64.85 | 454.44 | 461.43742015467615 |
| 18.32 | 42.28 | 1007.86 | 45.62 | 460.27 | 463.53345887643894 |
| 18.32 | 65.94 | 1012.74 | 86.77 | 450.5 | 452.02894677235804 |
| 18.34 | 44.63 | 1000.76 | 89.27 | 455.22 | 455.963340998892 |
| 18.34 | 50.59 | 1018.42 | 83.95 | 457.17 | 456.69115708185933 |
| 18.36 | 53.16 | 1013.31 | 83.18 | 458.47 | 455.70816977608035 |
| 18.36 | 56.65 | 1020.29 | 82.0 | 456.49 | 455.5784197907349 |
| 18.38 | 55.28 | 1020.22 | 68.33 | 451.29 | 457.8698842565306 |
| 18.4 | 50.16 | 1011.51 | 98.07 | 453.78 | 454.0602820488982 |
| 18.41 | 42.44 | 1012.66 | 65.93 | 463.2 | 460.7479119618785 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 | 453.92151217696727 |
| 18.42 | 60.1 | 1009.77 | 86.75 | 451.93 | 453.0532072948905 |
| 18.42 | 63.94 | 1020.47 | 74.47 | 450.55 | 454.758298968509 |
| 18.48 | 46.48 | 1007.53 | 82.57 | 461.49 | 456.7605922899199 |
| 18.48 | 58.59 | 1015.61 | 85.14 | 452.52 | 454.0242328275001 |
| 18.5 | 52.08 | 1006.23 | 100.09 | 451.23 | 452.6641989591518 |
| 18.51 | 48.06 | 1015.14 | 79.83 | 455.44 | 457.32803096446935 |
| 18.53 | 63.91 | 1010.26 | 97.8 | 440.64 | 450.3190565318654 |
| 18.55 | 41.85 | 1015.24 | 62.47 | 467.51 | 461.3397464375879 |
| 18.55 | 46.48 | 1007.34 | 80.67 | 452.37 | 456.8872770659861 |
| 18.56 | 42.28 | 1007.75 | 60.89 | 462.05 | 460.8339970164602 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 | 462.10615685280686 |
| 18.63 | 45.87 | 1007.98 | 79.9 | 453.79 | 457.0494808979769 |
| 18.65 | 52.08 | 1005.48 | 99.94 | 449.55 | 452.3356980438659 |
| 18.66 | 56.53 | 1020.13 | 80.04 | 459.45 | 455.30258286482143 |
| 18.68 | 43.69 | 1016.68 | 48.88 | 463.02 | 462.7299869900826 |
| 18.68 | 56.65 | 1020.38 | 80.26 | 455.79 | 455.2223463598715 |
| 18.68 | 62.1 | 1019.78 | 83.67 | 453.25 | 453.31727625144936 |
| 18.73 | 40.12 | 1013.19 | 74.12 | 454.71 | 459.55748654713716 |
| 18.73 | 46.48 | 1007.19 | 79.23 | 450.74 | 456.7379403460756 |
| 18.8 | 47.83 | 1005.86 | 76.77 | 453.9 | 456.5169354267787 |
| 18.81 | 52.08 | 1004.43 | 99.6 | 450.87 | 451.9912034594031 |
| 18.83 | 48.98 | 1016.33 | 74.23 | 453.28 | 457.39523074964615 |
| 18.84 | 61.27 | 1019.64 | 71.95 | 454.47 | 454.91390716792046 |
| 18.86 | 50.78 | 1008.46 | 91.67 | 446.7 | 453.70377279073705 |
| 18.87 | 43.43 | 1009.16 | 69.5 | 454.58 | 458.80810089986693 |
| 18.87 | 52.05 | 1012.02 | 53.46 | 458.64 | 459.2317295422404 |
| 18.89 | 66.86 | 1012.38 | 71.96 | 454.02 | 452.8313052555021 |
| 18.9 | 62.96 | 1020.69 | 80.57 | 455.88 | 453.2048261109298 |
| 18.91 | 43.14 | 1013.56 | 58.34 | 460.19 | 460.78946144208953 |
| 18.95 | 46.21 | 1013.47 | 81.22 | 457.58 | 456.60185019595883 |
| 18.97 | 50.59 | 1016.01 | 74.9 | 459.68 | 456.60000256329795 |
| 18.98 | 38.52 | 1018.85 | 63.16 | 454.6 | 461.5337919917975 |
| 18.99 | 56.65 | 1020.46 | 77.16 | 457.55 | 455.08314377928764 |
| 19.02 | 50.66 | 1013.04 | 85.25 | 455.42 | 454.7344713102365 |
| 19.05 | 53.16 | 1013.22 | 82.8 | 455.76 | 454.4253732327452 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 | 450.84382297953874 |
| 19.06 | 56.65 | 1020.82 | 82.14 | 455.7 | 454.2509501800389 |
| 19.08 | 44.63 | 1020.05 | 41.07 | 455.95 | 463.1377560912732 |
| 19.08 | 58.59 | 1013.42 | 68.88 | 451.05 | 455.0606464477159 |
| 19.11 | 58.95 | 1017.07 | 85.49 | 449.89 | 452.78710305999687 |
| 19.12 | 50.16 | 1011.52 | 99.71 | 451.49 | 452.43308379405045 |
| 19.13 | 42.18 | 1001.45 | 98.77 | 456.04 | 453.7206916287245 |
| 19.14 | 56.65 | 1020.84 | 82.97 | 458.06 | 453.97719041615505 |
| 19.2 | 58.71 | 1009.8 | 84.62 | 448.17 | 452.20842313451385 |
| 19.22 | 62.1 | 1019.43 | 79.19 | 451.8 | 452.90074763749135 |
| 19.23 | 41.67 | 1012.53 | 48.86 | 465.66 | 461.8378158906793 |
| 19.24 | 58.33 | 1013.65 | 85.47 | 449.26 | 452.41543202652065 |
| 19.25 | 43.43 | 1012.01 | 73.26 | 451.08 | 457.75864371455697 |
| 19.26 | 44.34 | 1019.45 | 51.32 | 467.72 | 461.3187533462041 |
| 19.31 | 43.56 | 1013.65 | 41.54 | 463.35 | 462.37131638295244 |
| 19.31 | 60.07 | 1014.86 | 69.37 | 453.47 | 454.29376940501464 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 | 453.15381920341724 |
| 19.43 | 52.9 | 1018.35 | 61.12 | 456.08 | 457.3375291656138 |
| 19.44 | 59.21 | 1018.5 | 88.35 | 448.04 | 451.78495851322157 |
| 19.47 | 58.79 | 1016.8 | 87.26 | 450.17 | 451.8524216632198 |
| 19.54 | 44.57 | 1007.19 | 78.93 | 450.54 | 455.69553323527913 |
| 19.54 | 50.66 | 1014.7 | 84.53 | 456.61 | 453.9716415466405 |
| 19.57 | 68.61 | 1011.13 | 96.4 | 448.73 | 447.41632457686086 |
| 19.6 | 48.14 | 1013.18 | 68.71 | 456.57 | 456.66826631159057 |
| 19.6 | 60.95 | 1015.4 | 84.26 | 456.06 | 451.3868160236597 |
| 19.61 | 56.65 | 1020.64 | 63.74 | 457.41 | 455.8596185860852 |
| 19.62 | 58.79 | 1017.59 | 87.39 | 446.29 | 451.60844251280884 |
| 19.62 | 68.63 | 1012.26 | 68.05 | 453.84 | 451.542577765937 |
| 19.64 | 56.65 | 1020.79 | 73.88 | 456.03 | 454.3347436794228 |
| 19.65 | 42.23 | 1013.04 | 75.9 | 461.16 | 456.98501253896984 |
| 19.65 | 57.85 | 1011.73 | 93.89 | 450.5 | 450.35966629930124 |
| 19.66 | 51.43 | 1010.17 | 84.19 | 459.12 | 453.2290277149437 |
| 19.67 | 60.77 | 1017.33 | 88.13 | 444.87 | 450.8892362675085 |
| 19.68 | 44.34 | 1019.49 | 49.5 | 468.27 | 460.77739524450277 |
| 19.68 | 51.19 | 1008.64 | 94.88 | 452.04 | 451.5662781980391 |
| 19.68 | 56.65 | 1020.75 | 67.25 | 456.89 | 455.22151625901614 |
| 19.69 | 39.72 | 1001.49 | 60.34 | 456.55 | 458.86327155628703 |
| 19.69 | 56.65 | 1020.84 | 72.14 | 455.07 | 454.4962025118092 |
| 19.7 | 51.3 | 1014.88 | 88.1 | 454.94 | 452.9973261711798 |
| 19.7 | 52.84 | 1004.86 | 89.72 | 444.64 | 451.56134671760844 |
| 19.72 | 44.78 | 1009.27 | 39.18 | 464.54 | 461.26403285215525 |
| 19.73 | 49.69 | 1007.78 | 73.02 | 454.28 | 454.96273138933435 |
| 19.73 | 68.63 | 1012.41 | 74.12 | 450.26 | 450.45712572408434 |
| 19.76 | 62.1 | 1020.07 | 73.55 | 450.44 | 452.7340333117575 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 | 451.3669335966618 |
| 19.79 | 60.1 | 1010.47 | 84.04 | 452.41 | 450.86300710254875 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 | 450.87541624186673 |
| 19.8 | 58.79 | 1017.04 | 87.71 | 449.66 | 451.1697942873412 |
| 19.8 | 67.71 | 1005.58 | 69.65 | 446.03 | 450.6475445784032 |
| 19.82 | 46.63 | 1013.17 | 87.1 | 456.36 | 453.93684539976664 |
| 19.83 | 46.33 | 1013.27 | 96.4 | 451.22 | 452.6438110022995 |
| 19.83 | 59.21 | 1012.67 | 96.42 | 440.03 | 449.38085095551605 |
| 19.86 | 41.67 | 1012.31 | 53.9 | 462.86 | 459.86949886435633 |
| 19.87 | 48.14 | 1016.94 | 81.56 | 451.14 | 454.5790164502554 |
| 19.87 | 49.69 | 1012.23 | 68.57 | 456.03 | 455.7041227162542 |
| 19.89 | 50.78 | 1008.85 | 92.97 | 446.35 | 451.5591661954991 |
| 19.89 | 51.43 | 1007.38 | 91.79 | 448.85 | 451.4495789708702 |
| 19.89 | 68.08 | 1012.65 | 80.25 | 448.71 | 449.41092940189 |
| 19.92 | 46.97 | 1014.32 | 69.17 | 459.39 | 456.368436088565 |
| 19.93 | 56.65 | 1020.7 | 62.82 | 456.53 | 455.3814815227917 |
| 19.94 | 44.63 | 1004.73 | 78.48 | 455.58 | 454.77441817350825 |
| 19.94 | 44.9 | 1008.52 | 74.69 | 459.47 | 455.56852272126105 |
| 19.94 | 56.53 | 1020.48 | 76.43 | 453.8 | 453.3887778929569 |
| 19.95 | 58.46 | 1017.45 | 89.46 | 447.1 | 450.7408294300028 |
| 19.97 | 50.78 | 1008.75 | 92.7 | 446.57 | 451.436105216529 |
| 19.99 | 40.79 | 1003.15 | 87.55 | 455.28 | 454.1835978057384 |
| 20.01 | 45.09 | 1014.21 | 38.19 | 453.96 | 461.1739549532308 |
| 20.01 | 68.63 | 1012.34 | 69.49 | 454.5 | 450.58677338372456 |
| 20.03 | 60.77 | 1017.23 | 87.82 | 449.31 | 450.2319334353776 |
| 20.04 | 49.39 | 1020.62 | 78.84 | 449.63 | 454.6358406173096 |
| 20.04 | 58.2 | 1017.56 | 74.31 | 448.92 | 452.85108866192866 |
| 20.08 | 54.42 | 1011.79 | 89.35 | 457.14 | 451.05259673451775 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 | 451.5232844413466 |
| 20.1 | 57.17 | 1011.96 | 87.68 | 452.67 | 450.58585768893414 |
| 20.11 | 51.19 | 1007.82 | 92.06 | 449.03 | 451.0815006280822 |
| 20.12 | 58.12 | 1015.47 | 79.38 | 453.33 | 451.80697350389795 |
| 20.16 | 57.76 | 1019.34 | 72.1 | 455.13 | 453.19662660647845 |
| 20.19 | 44.57 | 1009.2 | 72.13 | 454.36 | 455.59739505823774 |
| 20.19 | 66.86 | 1012.97 | 64.7 | 454.84 | 451.430922332517 |
| 20.21 | 54.9 | 1016.82 | 66.56 | 454.23 | 454.4162557726164 |
| 20.21 | 69.94 | 1009.33 | 83.96 | 447.06 | 447.51848167260005 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 | 457.4899833309971 |
| 20.24 | 56.65 | 1020.72 | 62.9 | 455.49 | 454.7734968589603 |
| 20.25 | 44.78 | 1007.93 | 40.16 | 462.44 | 459.9896954813451 |
| 20.28 | 48.78 | 1017.4 | 82.51 | 451.59 | 453.5274885789799 |
| 20.28 | 62.52 | 1017.89 | 75.67 | 452.45 | 451.13958592197287 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 | 451.01129177115814 |
| 20.33 | 57.76 | 1016.47 | 75.35 | 450.25 | 452.16097295111786 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 | 455.0355456385039 |
| 20.43 | 63.09 | 1016.46 | 91.78 | 445.58 | 448.2416124360999 |
| 20.45 | 59.8 | 1015.13 | 79.21 | 452.96 | 450.748723139911 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 | 453.9480760674323 |
| 20.51 | 39.72 | 1002.25 | 47.97 | 452.39 | 459.1480198621134 |
| 20.51 | 68.08 | 1012.73 | 78.05 | 445.96 | 448.54249260358677 |
| 20.56 | 60.08 | 1017.79 | 78.08 | 452.8 | 450.84813038147 |
| 20.56 | 64.45 | 1012.24 | 53.09 | 458.97 | 452.9523382793844 |
| 20.58 | 39.53 | 1005.68 | 62.09 | 460.1 | 457.2797775931854 |
| 20.59 | 59.8 | 1015.27 | 77.94 | 453.83 | 450.67534900317014 |
| 20.6 | 59.15 | 1013.32 | 91.07 | 443.76 | 448.7439698617689 |
| 20.61 | 60.1 | 1010.84 | 80.57 | 450.46 | 449.81767655065505 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 | 449.46273191454395 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 | 450.23276134284185 |
| 20.61 | 65.61 | 1014.91 | 83.82 | 449.72 | 448.3011628860887 |
| 20.64 | 61.86 | 1012.81 | 99.97 | 447.14 | 446.65132022669536 |
| 20.65 | 41.67 | 1012.76 | 45.27 | 455.5 | 459.6412858596406 |
| 20.65 | 57.5 | 1016.04 | 87.45 | 448.22 | 449.8084139802319 |
| 20.68 | 63.86 | 1015.73 | 74.36 | 447.84 | 450.0492245621943 |
| 20.69 | 50.78 | 1008.71 | 91.95 | 447.58 | 450.1534891767275 |
| 20.71 | 58.18 | 1007.63 | 98.44 | 447.06 | 447.2352893702614 |
| 20.72 | 63.94 | 1017.17 | 59.83 | 447.69 | 452.1889854808296 |
| 20.76 | 44.58 | 1017.09 | 57.47 | 462.01 | 457.2763644769643 |
| 20.76 | 59.04 | 1012.51 | 85.39 | 448.92 | 449.22543586447597 |
| 20.76 | 69.05 | 1001.89 | 77.86 | 442.82 | 446.9636998742384 |
| 20.77 | 43.77 | 1010.76 | 63.12 | 453.46 | 456.1194901357003 |
| 20.78 | 58.86 | 1016.02 | 77.29 | 446.2 | 450.6991034809647 |
| 20.8 | 69.45 | 1013.7 | 82.48 | 443.77 | 447.0742816761689 |
| 20.82 | 63.77 | 1014.28 | 86.37 | 448.9 | 447.93156732156075 |
| 20.83 | 44.78 | 1008.51 | 35.9 | 460.6 | 459.53962876300176 |
| 20.85 | 59.21 | 1012.9 | 75.97 | 444.44 | 450.41539217977487 |
| 20.87 | 57.19 | 1006.5 | 77.0 | 445.95 | 450.2091683575852 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 | 450.5270199129538 |
| 20.9 | 67.07 | 1005.43 | 82.85 | 443.46 | 446.7475520504839 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 | 444.5071996193093 |
| 20.94 | 44.78 | 1008.14 | 35.7 | 465.57 | 459.3265106832487 |
| 20.94 | 68.12 | 1012.43 | 78.2 | 446.41 | 447.6568115900025 |
| 20.95 | 44.89 | 1010.48 | 67.97 | 450.39 | 454.7627521673882 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 | 454.2185133216816 |
| 20.95 | 70.72 | 1009.96 | 87.73 | 445.66 | 445.39798853968557 |
| 20.96 | 69.48 | 1011.04 | 82.63 | 444.31 | 446.5197598700026 |
| 20.98 | 60.1 | 1011.07 | 79.44 | 450.95 | 449.2875712413884 |
| 20.99 | 67.07 | 1005.17 | 82.41 | 442.02 | 446.61697690688305 |
| 21.01 | 58.96 | 1014.33 | 61.8 | 453.88 | 452.35263519535 |
| 21.06 | 50.59 | 1016.42 | 66.12 | 454.15 | 453.8829146493028 |
| 21.06 | 62.91 | 1011.92 | 75.52 | 455.22 | 449.0737291884585 |
| 21.06 | 67.07 | 1004.9 | 84.09 | 446.44 | 446.2148995417375 |
| 21.08 | 44.05 | 1008.13 | 72.52 | 449.6 | 453.8663667211949 |
| 21.11 | 58.66 | 1011.7 | 68.71 | 457.89 | 451.0124137711604 |
| 21.11 | 59.39 | 1013.87 | 85.29 | 446.02 | 448.5883814092364 |
| 21.13 | 51.43 | 1007.43 | 88.72 | 445.4 | 449.5097304398748 |
| 21.14 | 58.05 | 1012.98 | 87.27 | 449.74 | 448.5033053489824 |
| 21.14 | 58.98 | 1009.05 | 94.36 | 448.31 | 446.9172206057899 |
| 21.16 | 45.38 | 1014.65 | 73.06 | 458.63 | 453.8324720669487 |
| 21.18 | 44.57 | 1007.27 | 73.67 | 449.93 | 453.30606496401464 |
| 21.18 | 60.1 | 1011.02 | 78.19 | 452.39 | 449.08008122232087 |
| 21.19 | 74.93 | 1015.75 | 80.84 | 443.29 | 445.36190546480196 |
| 21.26 | 50.32 | 1008.41 | 87.22 | 446.22 | 449.83432112958235 |
| 21.28 | 70.32 | 1011.06 | 90.12 | 439.0 | 444.60209183702824 |
| 21.32 | 45.01 | 1012.23 | 59.94 | 452.75 | 455.3330390953461 |
| 21.32 | 49.02 | 1008.81 | 85.81 | 445.65 | 450.28095549994475 |
| 21.33 | 58.46 | 1016.17 | 79.73 | 445.27 | 449.3942290809264 |
| 21.33 | 60.1 | 1010.97 | 78.36 | 451.95 | 448.76188428428145 |
| 21.33 | 63.86 | 1020.33 | 72.13 | 445.02 | 449.49526226001734 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 | 449.4914112072868 |
| 21.36 | 58.95 | 1018.35 | 78.87 | 443.93 | 449.5171242611325 |
| 21.36 | 68.28 | 1007.6 | 72.37 | 445.91 | 447.2640843894497 |
| 21.38 | 44.05 | 1005.69 | 81.66 | 445.71 | 451.75573664689705 |
| 21.39 | 51.3 | 1013.39 | 89.05 | 452.7 | 449.47769197848703 |
| 21.39 | 63.9 | 1013.44 | 70.95 | 449.38 | 448.9807967933042 |
| 21.4 | 44.57 | 1005.7 | 73.09 | 445.09 | 452.83851864090616 |
| 21.4 | 68.28 | 1008.2 | 60.34 | 450.22 | 448.99070865761826 |
| 21.41 | 56.9 | 1007.03 | 79.41 | 441.41 | 448.9314689751323 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 | 458.19122302999057 |
| 21.44 | 51.19 | 1009.1 | 84.94 | 446.17 | 449.6590005617065 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 | 446.545237358833 |
| 21.45 | 45.09 | 1013.83 | 52.26 | 453.15 | 456.3129527756159 |
| 21.45 | 60.08 | 1017.92 | 72.7 | 451.49 | 449.9268730104538 |
| 21.45 | 66.05 | 1014.81 | 73.73 | 453.38 | 448.0350183689646 |
| 21.46 | 46.63 | 1012.97 | 71.29 | 452.1 | 453.06361431502756 |
| 21.47 | 58.79 | 1017.0 | 76.97 | 446.33 | 449.51211271813366 |
| 21.49 | 56.9 | 1007.47 | 66.66 | 452.46 | 450.672947544889 |
| 21.52 | 66.51 | 1015.32 | 72.85 | 444.87 | 447.9552056621943 |
| 21.53 | 52.84 | 1005.06 | 88.22 | 444.04 | 448.2666586698033 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 | 448.66968221055265 |
| 21.54 | 69.48 | 1011.04 | 80.48 | 443.15 | 445.7146703190735 |
| 21.55 | 44.57 | 1006.03 | 71.54 | 446.84 | 452.8021698612517 |
| 21.55 | 60.27 | 1017.42 | 92.59 | 443.93 | 446.7443660008083 |
| 21.57 | 66.49 | 1014.76 | 68.19 | 455.18 | 448.49796091182566 |
| 21.58 | 63.87 | 1015.27 | 63.15 | 451.88 | 449.90863388073797 |
| 21.61 | 41.54 | 1014.5 | 75.62 | 458.47 | 453.53620348431275 |
| 21.61 | 49.39 | 1019.41 | 78.2 | 449.26 | 451.60241101975805 |
| 21.62 | 50.05 | 1007.2 | 92.9 | 444.16 | 448.28015134415546 |
| 21.65 | 58.18 | 1008.33 | 95.28 | 441.05 | 445.940139097113 |
| 21.69 | 44.57 | 1005.84 | 71.53 | 447.88 | 452.5181226448485 |
| 21.71 | 65.27 | 1013.24 | 63.58 | 444.91 | 449.080854354919 |
| 21.73 | 69.05 | 1001.31 | 86.64 | 439.01 | 443.764677908941 |
| 21.75 | 59.8 | 1016.65 | 72.94 | 453.17 | 449.2796285666682 |
Now that we have real predictions we can use an evaluation metric such as Root Mean Squared Error to validate our regression model. The lower the Root Mean Squared Error, the better our model.
//Now let's compute some evaluation metrics against our test dataset
import org.apache.spark.mllib.evaluation.RegressionMetrics
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
import org.apache.spark.mllib.evaluation.RegressionMetrics
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@2cb97126
val rmse = metrics.rootMeanSquaredError
rmse: Double = 4.426730543741181
val explainedVariance = metrics.explainedVariance
explainedVariance: Double = 269.3177257387032
val r2 = metrics.r2
r2: Double = 0.9314313152952873
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 4.426730543741181
Explained Variance: 269.3177257387032
R2: 0.9314313152952873
Generally a good model will have 68% of predictions within 1 RMSE and 95% within 2 RMSE of the actual value. Let's calculate and see if our RMSE meets this criteria.
display(predictionsAndLabels) // recall the DataFrame predictionsAndLabels
// First we calculate the residual error and divide it by the RMSE from predictionsAndLabels DataFrame and make another DataFrame that is registered as a temporary table Power_Plant_RMSE_Evaluation
predictionsAndLabels.selectExpr("PE", "Predicted_PE", "PE - Predicted_PE AS Residual_Error", s""" (PE - Predicted_PE) / $rmse AS Within_RSME""").createOrReplaceTempView("Power_Plant_RMSE_Evaluation")
SELECT * from Power_Plant_RMSE_Evaluation
| PE | Predicted_PE | Residual_Error | Within_RSME |
|---|---|---|---|
| 490.34 | 493.23742177145215 | -2.897421771452173 | -0.6545286058914855 |
| 482.66 | 488.93663844863084 | -6.27663844863082 | -1.4178948518800556 |
| 489.04 | 488.2642491377776 | 0.7757508622224236 | 0.17524239493619823 |
| 489.64 | 487.68500173970443 | 1.9549982602955538 | 0.44163480044197995 |
| 489.0 | 487.8432005878593 | 1.1567994121406855 | 0.26132139752130357 |
| 488.03 | 486.7875685475632 | 1.24243145243679 | 0.28066570579802425 |
| 491.9 | 485.8682911927764 | 6.031708807223595 | 1.3625651590092023 |
| 489.36 | 485.34119715268844 | 4.018802847311576 | 0.9078489886839033 |
| 481.47 | 482.9938172155284 | -1.5238172155283678 | -0.3442308494884213 |
| 482.05 | 482.5648390621137 | -0.5148390621137082 | -0.11630232674577932 |
| 494.75 | 487.00024243767876 | 7.749757562321236 | 1.7506729821805804 |
| 482.98 | 483.3467525779819 | -0.3667525779818561 | -8.284953745386567e-2 |
| 475.34 | 482.8336530053327 | -7.493653005332703 | -1.692818871916148 |
| 483.73 | 483.6145343498689 | 0.11546565013111376 | 2.608373132048146e-2 |
| 488.42 | 484.53187599470635 | 3.888124005293662 | 0.8783285919200484 |
| 495.35 | 487.2657538698361 | 8.084246130163933 | 1.8262340682999108 |
| 491.32 | 487.10787889447494 | 4.212121105525057 | 0.9515196517846441 |
| 486.46 | 482.0547750131424 | 4.405224986857604 | 0.9951418870719423 |
| 483.12 | 483.688095258955 | -0.5680952589549975 | -0.12833292050229034 |
| 495.23 | 487.0194077282659 | 8.210592271734129 | 1.8547757064958097 |
| 479.91 | 480.1986449575354 | -0.2886449575353822 | -6.520499829010114e-2 |
| 492.12 | 484.35541367676683 | 7.764586323233175 | 1.7540228045303743 |
| 491.38 | 483.7973981417879 | 7.582601858212115 | 1.712912449331917 |
| 481.56 | 484.07023605498495 | -2.5102360549849436 | -0.567063215206105 |
| 491.84 | 484.05572584065357 | 7.78427415934641 | 1.758470293691663 |
| 484.64 | 483.0334383183464 | 1.6065616816536021 | 0.36292285373571487 |
| 490.07 | 483.9952002249004 | 6.0747997750996205 | 1.3722994239368362 |
| 478.02 | 480.02034741363497 | -2.0003474136349837 | -0.4518791902667791 |
| 476.61 | 479.7602256710553 | -3.1502256710552956 | -0.711637096481805 |
| 487.09 | 483.7798894431585 | 3.3101105568414937 | 0.7477551488923485 |
| 482.82 | 483.2217349280458 | -0.401734928045812 | -9.075206274161249e-2 |
| 481.6 | 480.1171178824119 | 1.482882117588133 | 0.33498359634397323 |
| 470.02 | 482.0478125504672 | -12.027812550467218 | -2.717087121436152 |
| 489.05 | 481.81327159305386 | 7.236728406946156 | 1.6347795140090344 |
| 487.03 | 484.6333949755666 | 2.39660502443337 | 0.5413939250993844 |
| 490.57 | 482.2826790358646 | 8.287320964135404 | 1.8721087453250556 |
| 488.57 | 482.86050781997415 | 5.709492180025848 | 1.2897763086344445 |
| 480.39 | 483.17821215232124 | -2.7882121523212504 | -0.6298581141929721 |
| 481.37 | 483.0829476732433 | -1.7129476732433204 | -0.3869554869711247 |
| 489.62 | 482.71830544679335 | 6.901694553206653 | 1.5590952476122018 |
| 481.13 | 481.35862683823984 | -0.22862683823984753 | -5.164688385271067e-2 |
| 485.94 | 481.4164995749685 | 4.5235004250315 | 1.021860350507925 |
| 482.49 | 482.3037528502627 | 0.18624714973731216 | 4.2073297187840204e-2 |
| 483.77 | 483.6070229944035 | 0.1629770055964741 | 3.6816563372465104e-2 |
| 491.77 | 481.0249164343975 | 10.7450835656025 | 2.4273181887690556 |
| 483.43 | 482.2081944229683 | 1.2218055770317164 | 0.2760063132279849 |
| 481.49 | 482.6905244198958 | -1.2005244198957712 | -0.2711988922825122 |
| 478.78 | 480.9741288614041 | -2.194128861404124 | -0.49565448805243767 |
| 492.96 | 485.8565717694332 | 7.103428230566806 | 1.6046669568831393 |
| 481.36 | 483.99243959507345 | -2.6324395950734356 | -0.5946690382578993 |
| 486.68 | 481.76650494734884 | 4.913495052651172 | 1.1099602752189677 |
| 484.94 | 483.069724719673 | 1.8702752803270073 | 0.42249584921569994 |
| 483.01 | 481.33834961288795 | 1.671650387112038 | 0.3776264153858503 |
| 488.17 | 483.1883946104104 | 4.9816053895896175 | 1.1253464244922151 |
| 490.41 | 480.1679106982307 | 10.24208930176934 | 2.313691606156222 |
| 483.11 | 482.4149038683481 | 0.6950961316518942 | 0.15702246269194528 |
| 485.89 | 480.94068220101354 | 4.949317798986442 | 1.1180526463224945 |
| 487.08 | 482.508645049916 | 4.571354950083958 | 1.0326707046913566 |
| 477.8 | 480.01746628251317 | -2.2174662825131577 | -0.5009264197587914 |
| 489.96 | 480.63940670945976 | 9.320593290540216 | 2.1055253303633577 |
| 486.92 | 483.0108446487773 | 3.9091553512226938 | 0.883079580425271 |
| 486.37 | 482.57972730795177 | 3.7902726920482337 | 0.8562239455498787 |
| 488.2 | 483.51586402481416 | 4.684135975185825 | 1.0581479782654901 |
| 479.06 | 481.6634377951154 | -2.603437795115383 | -0.5881175213603873 |
| 480.05 | 484.68450470880435 | -4.634504708804343 | -1.0469362575856187 |
| 487.18 | 482.8218608903564 | 4.358139109643616 | 0.9845051707078616 |
| 480.47 | 481.8880244206695 | -1.4180244206694965 | -0.32033221960491765 |
| 480.36 | 480.6306788292839 | -0.2706788292838951 | -6.11464435454739e-2 |
| 487.85 | 479.89671820679695 | 7.953281793203075 | 1.7966491781271794 |
| 486.11 | 479.6914116057231 | 6.418588394276924 | 1.449961394951398 |
| 481.83 | 481.1970697561745 | 0.6329302438254558 | 0.1429791665816065 |
| 482.96 | 482.17217802621536 | 0.7878219737846166 | 0.17796926331974147 |
| 483.92 | 481.3074409763495 | 2.612559023650533 | 0.590178010121793 |
| 477.69 | 479.64992318380445 | -1.9599231838044489 | -0.4427473424095181 |
| 474.25 | 480.70714895561014 | -6.457148955610137 | -1.4586722394340677 |
| 479.08 | 479.7024675196716 | -0.6224675196716021 | -0.14061563348410486 |
| 478.89 | 481.00544489343537 | -2.115444893435381 | -0.4778797517789611 |
| 483.11 | 482.38901084355183 | 0.7209891564481836 | 0.16287170617772706 |
| 488.43 | 481.25646633043965 | 7.173533669560356 | 1.6205038004183012 |
| 483.28 | 480.33371483717946 | 2.946285162820516 | 0.6655668633335223 |
| 486.76 | 482.5311759738776 | 4.228824026122368 | 0.9552928474721311 |
| 476.58 | 480.67721106043905 | -4.097211060439065 | -0.9255614318409298 |
| 481.61 | 482.53257783094546 | -0.9225778309454427 | -0.20841065925050425 |
| 477.8 | 480.0330510466423 | -2.233051046642288 | -0.5044470235035043 |
| 475.32 | 479.3324132109004 | -4.012413210900434 | -0.9064055675522111 |
| 475.89 | 478.2499419685471 | -2.359941968547105 | -0.5331117277702286 |
| 485.03 | 480.3355565472517 | 4.69444345274826 | 1.0604764411029242 |
| 482.46 | 479.7315598782737 | 2.728440121726294 | 0.6163555912803304 |
| 484.57 | 477.91894784424176 | 6.6510521557582365 | 1.502475041125319 |
| 483.94 | 483.45191519870116 | 0.48808480129883947 | 0.11025852973791857 |
| 482.39 | 482.34452319301437 | 4.547680698561862e-2 | 1.027322682875218e-2 |
| 483.8 | 478.45760011663003 | 5.342399883369978 | 1.2068500286116204 |
| 482.22 | 478.7638216096892 | 3.4561783903108108 | 0.7807519242835766 |
| 481.52 | 479.1388800137473 | 2.3811199862526564 | 0.5378958494817917 |
| 483.79 | 477.1846287648614 | 6.605371235138648 | 1.4921557049542535 |
| 480.54 | 476.81117998099177 | 3.7288200190082534 | 0.8423417649127792 |
| 477.27 | 480.84424359067077 | -3.5742435906707897 | -0.8074228949228237 |
| 474.5 | 480.1364995691749 | -5.636499569174873 | -1.273287251952606 |
| 475.52 | 479.2235127059344 | -3.703512705934429 | -0.8366248339128552 |
| 481.44 | 478.91505388939413 | 2.5249461106058675 | 0.5703862219885535 |
| 480.6 | 476.210862698602 | 4.389137301398023 | 0.9915076732202934 |
| 487.19 | 481.0254321330136 | 6.164567866986374 | 1.392578067734949 |
| 475.42 | 480.36098930984286 | -4.940989309842848 | -1.1161712376708273 |
| 476.22 | 478.51210495606927 | -2.292104956069238 | -0.5177873225895746 |
| 485.13 | 479.57731721621883 | 5.552682783781165 | 1.2543530104022107 |
| 488.02 | 484.33140555054337 | 3.688594449456616 | 0.8332547944829863 |
| 477.78 | 478.4450809561189 | -0.6650809561189135 | -0.15024202389261102 |
| 477.2 | 480.5031526805204 | -3.303152680520384 | -0.7461833621634392 |
| 467.56 | 479.2169410835439 | -11.65694108354387 | -2.6333071255094267 |
| 485.18 | 477.6146348725092 | 7.565365127490793 | 1.7090186657480726 |
| 483.52 | 478.33312476559934 | 5.186875234400645 | 1.17171695524459 |
| 480.21 | 479.4778900760471 | 0.7321099239528621 | 0.16538389150158914 |
| 478.81 | 481.48536176155926 | -2.675361761559259 | -0.6043651708916123 |
| 484.67 | 477.9675154808001 | 6.702484519199913 | 1.5140936302699408 |
| 477.9 | 479.6817134321857 | -1.781713432185711 | -0.40248969630754267 |
| 475.17 | 477.5050067316079 | -2.335006731607905 | -0.5274788489010923 |
| 485.73 | 478.32045063849523 | 7.409549361504787 | 1.673819828943716 |
| 479.91 | 478.1399555772117 | 1.7700444227883168 | 0.3998536629456538 |
| 486.4 | 479.65037308403333 | 6.749626915966644 | 1.5247431144210337 |
| 480.15 | 477.1324329554068 | 3.017567044593193 | 0.681669465709775 |
| 480.58 | 482.82343628916425 | -2.243436289164265 | -0.5067930534728822 |
| 484.07 | 478.16947013024355 | 5.90052986975644 | 1.3329317905059794 |
| 481.89 | 480.6437911412516 | 1.246208858748389 | 0.2815190232236669 |
| 483.14 | 478.8928744938167 | 4.247125506183295 | 0.9594271583094607 |
| 479.31 | 476.1246111330079 | 3.1853888669921275 | 0.7195804749163808 |
| 484.21 | 477.288235770853 | 6.921764229146959 | 1.563628994525865 |
| 476.02 | 478.30600580479216 | -2.2860058047921825 | -0.5164095221527084 |
| 480.69 | 478.2957350932866 | 2.3942649067133743 | 0.5408652916763936 |
| 481.91 | 476.52270993699136 | 5.387290063008663 | 1.2169907361146226 |
| 485.06 | 478.99917896902446 | 6.060821030975546 | 1.3691416206809235 |
| 485.29 | 480.8674382556021 | 4.42256174439791 | 0.9990582667496749 |
| 482.39 | 481.1210453702997 | 1.2689546297002607 | 0.2866573009496584 |
| 478.25 | 478.8354389662744 | -0.5854389662744097 | -0.1322508701375881 |
| 475.79 | 476.29244393984663 | -0.5024439398466143 | -0.11350226422907182 |
| 485.87 | 479.0178844308566 | 6.852115569143393 | 1.5478953375265612 |
| 479.23 | 476.4582419334783 | 2.771758066521727 | 0.6261411303745675 |
| 478.61 | 480.1903466285615 | -1.5803466285614718 | -0.3570008639436786 |
| 479.94 | 479.4487267515188 | 0.4912732484812068 | 0.11097880108736301 |
| 479.25 | 479.3384367489267 | -8.84367489267106e-2 | -1.997789295120947e-2 |
| 480.99 | 477.44189376811596 | 3.5481062318840486 | 0.8015184563019332 |
| 481.07 | 476.33561360755584 | 4.734386392444151 | 1.0694995653480546 |
| 480.4 | 482.3769169335795 | -1.9769169335795027 | -0.44658623651141477 |
| 482.8 | 476.04420182940044 | 6.755798170599576 | 1.5261372030315674 |
| 480.08 | 479.65335282852305 | 0.42664717147692954 | 9.637974736911713e-2 |
| 475.0 | 478.6696951676176 | -3.66969516761759 | -0.8289854400119429 |
| 483.88 | 476.696926422392 | 7.183073577608013 | 1.6226588690300885 |
| 482.52 | 476.90393713153463 | 5.616062868465349 | 1.2686705940133918 |
| 478.45 | 479.2188844607746 | -0.7688844607745864 | -0.17369127241360752 |
| 482.3 | 475.81261283946816 | 6.487387160531853 | 1.465503060651426 |
| 472.45 | 476.2100211409317 | -3.7600211409317126 | -0.8493901094223798 |
| 480.2 | 480.2141595165933 | -1.415951659333814e-2 | -3.19863982083975e-3 |
| 478.38 | 474.9481764100585 | 3.431823589941473 | 0.775250166241454 |
| 472.77 | 479.30598211413803 | -6.535982114138051 | -1.476480677907779 |
| 483.53 | 476.3744577455605 | 7.155542254439467 | 1.6164395333609067 |
| 475.47 | 478.6980048877349 | -3.228004887734869 | -0.7292074491181413 |
| 467.24 | 476.41215657483474 | -9.17215657483473 | -2.0719934236347326 |
| 481.03 | 480.7338755379764 | 0.29612446202355613 | 6.689462100697262e-2 |
| 482.37 | 480.51130475015583 | 1.8586952498441747 | 0.41987991622217147 |
| 484.97 | 478.0206284049 | 6.949371595100047 | 1.5698655082870474 |
| 479.53 | 475.4980717304681 | 4.031928269531875 | 0.9108140262190783 |
| 482.8 | 476.4769333498689 | 6.323066650131125 | 1.4283829990671366 |
| 477.26 | 480.5658974037096 | -3.3058974037095936 | -0.7468033961054397 |
| 479.02 | 478.9378167534134 | 8.218324658656684e-2 | 1.8565224554443056e-2 |
| 476.69 | 479.483969889582 | -2.7939698895820015 | -0.631158789082455 |
| 476.62 | 476.72728884411293 | -0.10728884411292938 | -2.423658794064658e-2 |
| 475.69 | 475.1283411969007 | 0.5616588030993057 | 0.1268789228414677 |
| 482.95 | 477.43108128919135 | 5.518918710808634 | 1.2467256943415417 |
| 483.24 | 479.0201634085648 | 4.2198365914351825 | 0.9532625827884375 |
| 484.86 | 476.63324412261045 | 8.226755877389564 | 1.858427070746631 |
| 491.97 | 478.3337308000573 | 13.636269199942717 | 3.080438049074982 |
| 474.88 | 478.39283560851754 | -3.5128356085175483 | -0.7935508099728905 |
| 475.64 | 477.10087603877 | -1.4608760387700386 | -0.33001241533337206 |
| 484.96 | 478.16396362897893 | 6.796036371021046 | 1.5352270267793358 |
| 475.42 | 478.96772021173297 | -3.547720211732951 | -0.8014312542128782 |
| 480.38 | 475.6006326388746 | 4.7793673611254235 | 1.0796607821279804 |
| 478.82 | 474.9766634864767 | 3.8433365135232975 | 0.8682110816429235 |
| 482.55 | 478.786077505979 | 3.763922494021017 | 0.8502714264690703 |
| 480.14 | 478.9365615888623 | 1.2034384111377108 | 0.271857163937664 |
| 482.55 | 476.1723094438278 | 6.377690556172183 | 1.4407225588170043 |
| 477.91 | 478.2490255806092 | -0.33902558060918864 | -7.658599891256687e-2 |
| 476.25 | 474.4577268339357 | 1.7922731660643194 | 0.4048751439362759 |
| 471.13 | 478.43777544278043 | -7.307775442780439 | -1.6508290646045036 |
| 482.16 | 478.00197412694763 | 4.158025873052395 | 0.9392995195813987 |
| 480.87 | 479.3152324207446 | 1.5547675792553832 | 0.3512225476325008 |
| 477.34 | 477.2801935898294 | 5.980641017055177e-2 | 1.3510289270963245e-2 |
| 483.66 | 478.285031656868 | 5.374968343132025 | 1.214207255223955 |
| 481.95 | 475.9420528274367 | 6.007947172563263 | 1.357197397311141 |
| 479.68 | 476.17198214059135 | 3.5080178594086533 | 0.7924624787403274 |
| 478.66 | 474.34503134979343 | 4.314968650206595 | 0.9747529486084029 |
| 478.8 | 479.7691576930105 | -0.9691576930105157 | -0.2189330666129607 |
| 481.12 | 475.82678857769963 | 5.293211422300374 | 1.1957383378087658 |
| 476.54 | 478.0366119605891 | -1.496611960589064 | -0.33808517274788225 |
| 478.03 | 477.46151999839185 | 0.5684800016081226 | 0.12841983400410018 |
| 479.23 | 479.110912113517 | 0.1190878864830438 | 2.6901995797195863e-2 |
| 480.74 | 477.72481326338215 | 3.0151867366178635 | 0.6811317532938489 |
| 478.88 | 479.54303529435083 | -0.6630352943508342 | -0.14977990817360218 |
| 481.05 | 479.71268358186353 | 1.3373164181364814 | 0.3021002532054435 |
| 473.54 | 473.26068816423447 | 0.2793118357655544 | 6.309664277182285e-2 |
| 469.73 | 476.05175958076205 | -6.321759580762034 | -1.4280877316330394 |
| 475.51 | 476.88388448180046 | -1.3738844818004736 | -0.31036099175789383 |
| 476.67 | 475.61433131158714 | 1.0556686884128794 | 0.23847593115995214 |
| 472.16 | 477.7130066863276 | -5.553006686327592 | -1.2544261801023373 |
| 481.85 | 475.2673812565115 | 6.582618743488524 | 1.487015909020143 |
| 479.45 | 479.3846135509556 | 6.538644904441071e-2 | 1.4770822031817277e-2 |
| 482.38 | 475.5344685772051 | 6.84553142279492 | 1.5464079765310332 |
| 469.58 | 473.85672752722735 | -4.276727527227365 | -0.9661142653632034 |
| 477.93 | 477.3826135030455 | 0.5473864969545161 | 0.12365480382094843 |
| 478.21 | 477.2300569616709 | 0.9799430383290542 | 0.2213694799459989 |
| 477.62 | 479.265495182268 | -1.6454951822680073 | -0.371717945334288 |
| 472.46 | 472.7773808573311 | -0.3173808573311021 | -7.16964482466269e-2 |
| 467.37 | 473.2887424901299 | -5.918742490129887 | -1.3370460279083884 |
| 473.2 | 475.32128019247375 | -2.121280192473762 | -0.4791979479015218 |
| 480.05 | 476.83702080523557 | 3.2129791947644435 | 0.7258131397464832 |
| 469.65 | 473.90133694043874 | -4.251336940438762 | -0.9603785228015735 |
| 485.23 | 477.4312500724956 | 7.798749927504446 | 1.761740374853143 |
| 477.88 | 474.53026960482526 | 3.3497303951747313 | 0.756705284425051 |
| 475.21 | 475.5632280329792 | -0.35322803297924565 | -7.97943379405968e-2 |
| 475.91 | 476.45899184845075 | -0.5489918484507257 | -0.12401745329336308 |
| 477.85 | 475.16451288467897 | 2.6854871153210524 | 0.6066524918978817 |
| 464.86 | 472.95056743270436 | -8.090567432704347 | -1.827662052785967 |
| 466.05 | 473.09691475779204 | -7.046914757792024 | -1.591900543337891 |
| 463.16 | 472.93274352761154 | -9.77274352761151 | -2.2076662292962226 |
| 475.75 | 478.4561215361668 | -2.7061215361667905 | -0.6113138148859982 |
| 471.6 | 474.6981382928799 | -3.0981382928798666 | -0.6998705392764937 |
| 470.82 | 475.02803269176394 | -4.208032691763947 | -0.950596077665842 |
| 479.63 | 475.85397168574394 | 3.7760283142560525 | 0.8530061355541199 |
| 477.68 | 479.18053767427625 | -1.500537674276245 | -0.3389719928622738 |
| 478.73 | 473.5654621009553 | 5.164537899044717 | 1.166670943264595 |
| 473.66 | 477.1725989266351 | -3.5125989266350643 | -0.793497343451686 |
| 474.28 | 475.0636358283201 | -0.7836358283201434 | -0.17702361157448407 |
| 468.19 | 473.5875494551498 | -5.397549455149829 | -1.2193083364383357 |
| 463.57 | 472.67682233352394 | -9.10682233352395 | -2.0572343953484604 |
| 475.46 | 476.9235641778536 | -1.463564177853641 | -0.3306196669058454 |
| 473.05 | 471.68772310073444 | 1.3622768992655665 | 0.30773883474602903 |
| 474.92 | 475.9474342905188 | -1.0274342905187837 | -0.23209777066088685 |
| 481.31 | 474.932278891215 | 6.3777211087850105 | 1.4407294606630792 |
| 473.43 | 474.55112952359036 | -1.121129523590355 | -0.25326355704561365 |
| 477.27 | 477.46636654211125 | -0.1963665421112637 | -4.4359271514481574e-2 |
| 476.91 | 473.8650937482109 | 3.0449062517891434 | 0.6878454023126037 |
| 468.27 | 473.17098851617544 | -4.90098851617546 | -1.1071350441930146 |
| 480.11 | 474.8258713039414 | 5.284128696058588 | 1.1936865467290878 |
| 471.79 | 473.1211730372522 | -1.3311730372521993 | -0.3007124612846165 |
| 480.87 | 474.62974157133897 | 6.240258428661036 | 1.4096765924648265 |
| 477.53 | 474.801072598715 | 2.7289274012849773 | 0.6164656679054759 |
| 476.03 | 471.6665106288944 | 4.3634893711055724 | 0.9857137966698644 |
| 479.28 | 474.5250337253637 | 4.754966274636274 | 1.0741485680349747 |
| 470.76 | 473.3969220129792 | -2.6369220129791984 | -0.5956816180527323 |
| 477.65 | 475.74847822607404 | 1.9015217739259356 | 0.42955444320288233 |
| 474.16 | 472.3991408528041 | 1.7608591471959016 | 0.3977787059312943 |
| 476.31 | 477.2616855096003 | -0.9516855096002814 | -0.21498609418317555 |
| 479.17 | 474.9177051337058 | 4.252294866294221 | 0.9605949185920998 |
| 473.16 | 477.4228902388337 | -4.262890238833677 | -0.9629884170069595 |
| 478.03 | 473.50046202531144 | 4.529537974688537 | 1.0232242351169787 |
| 470.66 | 473.24796901874475 | -2.5879690187447295 | -0.5846231192914554 |
| 479.14 | 473.09058493481064 | 6.049415065189351 | 1.3665650089641064 |
| 480.04 | 472.3076103285209 | 7.732389671479098 | 1.746749569478921 |
| 472.54 | 471.615832151066 | 0.9241678489340188 | 0.20876984487810568 |
| 479.24 | 473.69713759778955 | 5.54286240221046 | 1.2521345827220822 |
| 474.42 | 472.19156900447234 | 2.228430995527674 | 0.5034033523179731 |
| 477.41 | 476.3350912306915 | 1.0749087693085357 | 0.2428222722587704 |
| 472.16 | 475.77435376625584 | -3.6143537662558174 | -0.8164837978146291 |
| 476.55 | 472.6471168581127 | 3.902883141887287 | 0.8816626861116392 |
| 474.24 | 472.62895527440253 | 1.6110447255974805 | 0.3639355749527803 |
| 474.91 | 472.5658087964315 | 2.3441912035685277 | 0.529553624374745 |
| 474.94 | 477.2343522450378 | -2.294352245037828 | -0.5182949859646964 |
| 478.47 | 473.38659302581107 | 5.083406974188961 | 1.1483434385624023 |
| 481.3 | 473.3858358704527 | 7.914164129547316 | 1.7878124840322416 |
| 477.19 | 472.2522916899094 | 4.937708310090613 | 1.1154300586630213 |
| 479.61 | 471.9871102146372 | 7.6228897853628155 | 1.7220135063654567 |
| 474.03 | 476.66325912606675 | -2.6332591260667755 | -0.5948541705999838 |
| 484.94 | 473.0585846959978 | 11.88141530400219 | 2.6840159315323495 |
| 478.76 | 472.5409240450936 | 6.219075954906373 | 1.4048914641301884 |
| 477.23 | 472.5905086170794 | 4.639491382920596 | 1.048062749037262 |
| 477.93 | 473.2433331311532 | 4.686666868846828 | 1.0587197080412232 |
| 483.54 | 473.33367076102724 | 10.206329238972785 | 2.305613395286325 |
| 470.66 | 472.25860679152254 | -1.5986067915225135 | -0.36112584123348884 |
| 468.57 | 472.68332438968736 | -4.113324389687364 | -0.9292014386335458 |
| 475.46 | 469.26491536026253 | 6.195084639737445 | 1.3994718175237673 |
| 467.6 | 476.77045249286635 | -9.170452492866332 | -2.07160847091363 |
| 479.16 | 472.2986932100967 | 6.861306789903324 | 1.5499716375563712 |
| 473.72 | 475.9280130742943 | -2.2080130742942856 | -0.49879093666907914 |
| 470.9 | 474.1703211862971 | -3.27032118629711 | -0.7387667159730148 |
| 479.2 | 473.8182300033406 | 5.381769996659386 | 1.215743751168344 |
| 476.32 | 474.4834318795844 | 1.8365681204156203 | 0.41488138983573053 |
| 477.34 | 473.75018039571677 | 3.589819604283207 | 0.8109415219227073 |
| 472.34 | 471.57861538146454 | 0.7613846185354305 | 0.17199705539157537 |
| 473.29 | 471.6415723647869 | 1.6484276352131246 | 0.37238038749473606 |
| 477.3 | 472.7701885173113 | 4.529811482688729 | 1.0232860206712358 |
| 472.11 | 471.77143688745184 | 0.3385631125481723 | 7.648152721354506e-2 |
| 468.09 | 474.5636411763966 | -6.4736411763965975 | -1.462397837959548 |
| 477.62 | 472.7148284274264 | 4.905171572573579 | 1.1080799981170868 |
| 468.84 | 472.4884135100371 | -3.6484135100371304 | -0.8241779060158768 |
| 477.2 | 474.2579394355058 | 2.94206056449417 | 0.6646125250731286 |
| 473.16 | 475.5315818680234 | -2.3715818680233838 | -0.5357411851906123 |
| 467.46 | 472.3352405654698 | -4.875240565469824 | -1.1013185729957693 |
| 476.24 | 471.7121476384473 | 4.527852361552732 | 1.0228434545117104 |
| 462.07 | 471.87019509945225 | -9.800195099452253 | -2.2138675491121655 |
| 478.3 | 473.39040211351204 | 4.909597886487973 | 1.1090799040003696 |
| 474.64 | 472.2988980097268 | 2.3411019902731596 | 0.528855769995572 |
| 476.18 | 473.7408434668035 | 2.439156533196524 | 0.5510063260220736 |
| 472.02 | 474.7548947976392 | -2.7348947976392424 | -0.6178137048585499 |
| 468.75 | 475.5773739728955 | -6.8273739728954865 | -1.5423062021582727 |
| 476.87 | 476.2403822241514 | 0.629617775848601 | 0.14223087889069697 |
| 472.27 | 469.24091010473035 | 3.0290898952696352 | 0.6842724817647582 |
| 476.7 | 472.7393314749417 | 3.9606685250582814 | 0.8947164246665408 |
| 473.93 | 472.63331852543564 | 1.296681474564366 | 0.2929208050392189 |
| 476.04 | 471.38775711954423 | 4.652242880455788 | 1.0509433168534397 |
| 471.92 | 475.2099855538805 | -3.289985553880456 | -0.7432089035850772 |
| 469.9 | 471.84963289726664 | -1.9496328972666674 | -0.4404227630306511 |
| 467.19 | 474.9342554366788 | -7.744255436678827 | -1.749430050046347 |
| 464.07 | 472.0765967355643 | -8.006596735564301 | -1.8086930425174812 |
| 472.45 | 477.1428353332941 | -4.692835333294113 | -1.0601131663478296 |
| 475.51 | 470.47813470324115 | 5.031865296758838 | 1.1367001553490168 |
| 476.91 | 473.32755876405025 | 3.582441235949773 | 0.8092747458990647 |
| 469.04 | 472.10000669812564 | -3.060006698125619 | -0.6912565984961676 |
| 474.49 | 471.88884153658796 | 2.601158463412048 | 0.5876026195201212 |
| 474.29 | 471.68655893301997 | 2.6034410669800536 | 0.588118260475777 |
| 465.61 | 472.62327183365306 | -7.01327183365305 | -1.5843005948416944 |
| 477.71 | 472.2836129719507 | 5.426387028049305 | 1.2258227543850637 |
| 460.6 | 470.4334505230224 | -9.83345052302235 | -2.221379961092406 |
| 474.72 | 471.91129640538503 | 2.8087035946149967 | 0.634487138275479 |
| 476.89 | 474.3054501914308 | 2.584549808569193 | 0.5838507185000019 |
| 471.56 | 474.6794786091428 | -3.1194786091427886 | -0.7046913242897344 |
| 473.54 | 475.187496898361 | -1.6474968983609983 | -0.37217013370970675 |
| 467.96 | 469.771457326924 | -1.8114573269240282 | -0.40920885267913865 |
| 475.13 | 472.08490735121984 | 3.0450926487801553 | 0.6878875094589886 |
| 470.88 | 473.8032225628117 | -2.923222562811702 | -0.6603570138111878 |
| 474.22 | 472.09588182193215 | 2.1241181780678744 | 0.47983904985387016 |
| 476.41 | 471.4926212025492 | 4.917378797450851 | 1.1108376145467862 |
| 465.45 | 471.19014476340215 | -5.7401447634021565 | -1.2967007380916762 |
| 478.51 | 471.43677609572336 | 7.073223904276631 | 1.597843788860663 |
| 464.43 | 469.7436046712689 | -5.313604671268877 | -1.2003451799842708 |
| 475.19 | 471.72684171223557 | 3.463158287764429 | 0.7823286855941306 |
| 476.12 | 474.3311768410223 | 1.7888231589777206 | 0.4040957861116898 |
| 466.52 | 470.11266519044227 | -3.5926651904422897 | -0.8115843408453783 |
| 466.75 | 469.0361408145477 | -2.286140814547707 | -0.5164400209043697 |
| 476.97 | 474.02676004123384 | 2.943239958766185 | 0.6648789506575099 |
| 476.73 | 470.9633331252549 | 5.766666874745113 | 1.302692092451488 |
| 473.82 | 471.12775460619287 | 2.692245393807127 | 0.6081791894050589 |
| 478.29 | 473.91084477665686 | 4.379155223343162 | 0.9892527182470402 |
| 473.79 | 470.2438958288006 | 3.546104171199431 | 0.8010661900831437 |
| 477.75 | 471.7153938676623 | 6.034606132337672 | 1.3632196657801583 |
| 470.33 | 474.55914246739445 | -4.2291424673944675 | -0.95536478346845 |
| 474.57 | 469.8009518761225 | 4.769048123877496 | 1.0773296627734226 |
| 464.57 | 470.2642322610151 | -5.694232261015088 | -1.2863290875171947 |
| 469.34 | 471.3638012594579 | -2.023801259457912 | -0.45717742235729764 |
| 465.82 | 471.92094622344274 | -6.100946223442747 | -1.3782059158917384 |
| 477.74 | 471.85580587680386 | 5.884194123196153 | 1.3292415395636934 |
| 474.28 | 470.30107562853505 | 3.9789243714649274 | 0.898840426845182 |
| 464.14 | 466.34356012780466 | -2.2035601278046784 | -0.49778501447760914 |
| 454.18 | 466.58075506687766 | -12.40075506687765 | -2.8013349681766146 |
| 467.21 | 469.3396022995035 | -2.1296022995035173 | -0.4810779148314091 |
| 470.36 | 471.72756140636227 | -1.367561406362256 | -0.30893260677359485 |
| 476.84 | 470.5786344502193 | 6.261365549780692 | 1.4144446986124883 |
| 474.35 | 470.6068799512958 | 3.7431200487042133 | 0.8455721466933415 |
| 477.61 | 472.5235663152981 | 5.086433684701888 | 1.149027173540852 |
| 478.1 | 471.9097423001995 | 6.1902576998004974 | 1.398381410079887 |
| 462.1 | 471.2847044384862 | -9.184704438486165 | -2.07482799048434 |
| 474.82 | 471.2662319288046 | 3.5537680711954067 | 0.802797467810633 |
| 463.03 | 469.73593028388524 | -6.705930283885266 | -1.5148720297345803 |
| 472.68 | 470.7066911497908 | 1.9733088502092073 | 0.4457711691982717 |
| 474.91 | 471.33177180371854 | 3.5782281962814864 | 0.8083230187436264 |
| 468.91 | 470.52692515963395 | -1.6169251596339222 | -0.3652639670874125 |
| 464.45 | 470.2092170118331 | -5.75921701183313 | -1.301009165777192 |
| 466.83 | 466.2832533837298 | 0.546746616270184 | 0.12351025454739102 |
| 475.24 | 470.18594349686214 | 5.054056503137872 | 1.1417131567412993 |
| 473.84 | 475.4613835085186 | -1.6213835085186474 | -0.36627110968185583 |
| 475.13 | 474.5994035325814 | 0.5305964674186043 | 0.11986193019333387 |
| 476.42 | 472.0863918606857 | 4.333608139314322 | 0.9789636158092969 |
| 474.46 | 470.7913069417126 | 3.6686930582873742 | 0.8287590631587972 |
| 468.13 | 467.56407826412726 | 0.5659217358727346 | 0.12784192086705481 |
| 470.27 | 471.5687225134983 | -1.2987225134983191 | -0.2933818764583589 |
| 471.6 | 469.9595844589464 | 1.6404155410536418 | 0.37057045258220545 |
| 472.49 | 471.6990473850642 | 0.7909526149358044 | 0.17867647626623853 |
| 479.32 | 473.0943993730868 | 6.225600626913206 | 1.4063653898508444 |
| 471.65 | 470.00140680122996 | 1.6485931987700155 | 0.3724177883609634 |
| 474.71 | 472.62554215897904 | 2.084457841020935 | 0.47087976564737744 |
| 467.2 | 468.51057584459073 | -1.310575844590744 | -0.29605954815653446 |
| 468.37 | 466.99762401287654 | 1.372375987123462 | 0.3100202222752913 |
| 464.4 | 467.7515630344035 | -3.3515630344035117 | -0.7571192782768728 |
| 467.47 | 468.17545309508614 | -0.7054530950861135 | -0.159362104405368 |
| 468.43 | 466.2393815815799 | 2.190618418420115 | 0.4948614777371899 |
| 466.05 | 470.09910156010346 | -4.049101560103452 | -0.914693478650593 |
| 471.1 | 470.4126440262426 | 0.6873559737574055 | 0.1552739582781331 |
| 468.01 | 470.1081379355774 | -2.0981379355774266 | -0.47397010386004174 |
| 477.41 | 473.04817642574005 | 4.361823574259972 | 0.9853374925715822 |
| 469.56 | 470.7822892277393 | -1.2222892277392816 | -0.27611557009437565 |
| 467.18 | 467.70575905298347 | -0.5257590529834602 | -0.11876915655659566 |
| 461.35 | 469.8226213597647 | -8.472621359764673 | -1.9139681704240736 |
| 474.4 | 474.3780743285961 | 2.192567140389201e-2 | 4.953016947212214e-3 |
| 473.26 | 468.30493209232344 | 4.9550679076765505 | 1.1193515979151634 |
| 470.04 | 469.5568353664925 | 0.4831646335074993 | 0.10914706208866293 |
| 472.31 | 471.0025099661929 | 1.3074900338070847 | 0.2953624624059635 |
| 467.19 | 469.9361134525985 | -2.746113452598479 | -0.6203480029931175 |
| 476.2 | 469.7928661275291 | 6.407133872470865 | 1.447373814412471 |
| 461.88 | 469.1737219130261 | -7.293721913026104 | -1.6476543672481883 |
| 471.24 | 474.2884906123142 | -3.048490612314197 | -0.688655110626592 |
| 473.02 | 468.06258267245875 | 4.957417327541236 | 1.119882332695939 |
| 471.32 | 473.485146860658 | -2.1651468606580124 | -0.489107443803926 |
| 474.23 | 472.61404029065585 | 1.6159597093441675 | 0.36504587152451 |
| 466.85 | 469.903915514171 | -3.053915514170967 | -0.6898805978802584 |
| 465.78 | 470.2469481759357 | -4.466948175935727 | -1.0090851773780105 |
| 473.46 | 467.632447347929 | 5.827552652071006 | 1.3164462111456963 |
| 466.64 | 470.9425442114299 | -4.302544211429904 | -0.9719462634817788 |
| 466.2 | 471.494284203131 | -5.294284203130985 | -1.195980679378918 |
| 467.28 | 468.30907898088435 | -1.0290789808843783 | -0.2324693067978492 |
| 475.73 | 468.87573922525866 | 6.854260774741363 | 1.5483799402320506 |
| 470.89 | 474.237754775417 | -3.347754775417002 | -0.7562589912210242 |
| 466.09 | 465.69130458295615 | 0.3986954170438253 | 9.006543612814395e-2 |
| 475.61 | 472.8180343417018 | 2.7919656582982384 | 0.6307060325245487 |
| 465.75 | 466.9269506815448 | -1.1769506815447812 | -0.2658735764273784 |
| 474.81 | 469.0391508364556 | 5.770849163544426 | 1.3036368729747179 |
| 474.26 | 468.1238036853617 | 6.136196314638312 | 1.3861689239960837 |
| 471.48 | 471.9340237695587 | -0.45402376955865975 | -0.10256413058630598 |
| 475.77 | 467.85769076077656 | 7.9123092392234184 | 1.7873934636501403 |
| 470.31 | 468.53947222591256 | 1.7705277740874408 | 0.39996285217557137 |
| 469.12 | 473.38202950699997 | -4.262029506999966 | -0.962793977380421 |
| 475.13 | 469.76472317857633 | 5.365276821423663 | 1.2120179370324367 |
| 467.41 | 468.04615604018346 | -0.6361560401834367 | -0.14370787512306984 |
| 469.83 | 469.00047164404333 | 0.8295283559566542 | 0.1873907498457296 |
| 474.46 | 474.0667420199239 | 0.39325798007610047 | 8.883711718846676e-2 |
| 472.18 | 467.138305828078 | 5.041694171922018 | 1.1389205017346977 |
| 468.46 | 468.5292032616302 | -6.920326163020718e-2 | -1.5633041348778628e-2 |
| 475.03 | 468.1969526319267 | 6.833047368073267 | 1.5435878241412961 |
| 473.49 | 469.4125049461586 | 4.077495053841403 | 0.9211075789572168 |
| 469.02 | 468.259374271566 | 0.760625728433979 | 0.17182562184847788 |
| 468.9 | 473.50603668317063 | -4.606036683170657 | -1.0405053204973118 |
| 471.19 | 468.47422142636185 | 2.715778573638147 | 0.6134953430761453 |
| 474.13 | 470.0383017110002 | 4.091698288999794 | 0.924316094817409 |
| 472.01 | 468.0562294553348 | 3.95377054466519 | 0.893158168449016 |
| 475.89 | 472.76694427363464 | 3.123055726365351 | 0.7054993963391207 |
| 471.0 | 466.0557279256278 | 4.944272074372179 | 1.1169128153424053 |
| 472.37 | 466.7407287783581 | 5.629271221641886 | 1.2716543656809969 |
| 466.08 | 464.79214736202914 | 1.2878526379708433 | 0.2909263677211387 |
| 471.61 | 470.21248865654456 | 1.3975113434554487 | 0.3156983081862408 |
| 471.44 | 472.8484021647681 | -1.4084021647681197 | -0.3181585485837661 |
| 462.3 | 470.24854120855605 | -7.948541208556037 | -1.79557827837392 |
| 461.49 | 470.2646001553115 | -8.774600155311475 | -1.9821852874504897 |
| 464.7 | 465.51076750880605 | -0.810767508806066 | -0.18315266781990724 |
| 467.68 | 466.3776971038656 | 1.3023028961343925 | 0.29419068616581573 |
| 472.41 | 466.5610830112806 | 5.848916988719452 | 1.321272422372547 |
| 468.58 | 470.92659992793443 | -2.3465999279344487 | -0.5300977560633852 |
| 472.22 | 468.98765579029424 | 3.2323442097057864 | 0.7301877034905364 |
| 469.18 | 467.5995301073336 | 1.5804698926664287 | 0.35702870934871034 |
| 473.88 | 474.44599166986796 | -0.5659916698679694 | -0.12785771898138407 |
| 461.12 | 467.5690713933053 | -6.449071393305303 | -1.4568475152442806 |
| 472.4 | 467.1768048505422 | 5.2231951494577515 | 1.1799216369387262 |
| 458.49 | 470.12758725908657 | -11.637587259086558 | -2.6289350896996853 |
| 475.36 | 469.2980914389405 | 6.061908561059511 | 1.36938729411264 |
| 473.0 | 469.1672380696391 | 3.8327619303609026 | 0.8658222795557158 |
| 461.44 | 470.1249314556345 | -8.684931455634512 | -1.961929096387823 |
| 463.9 | 471.81028761580865 | -7.91028761580867 | -1.7869367782036754 |
| 461.06 | 471.424799923348 | -10.364799923348016 | -2.3414119790965118 |
| 466.46 | 469.58969888462434 | -3.1296988846243607 | -0.7070000881461707 |
| 472.46 | 467.6133054100996 | 4.84669458990038 | 1.094870026989326 |
| 471.73 | 466.56182696263306 | 5.168173037366955 | 1.1674921223009784 |
| 471.05 | 469.14226719628124 | 1.9077328037187726 | 0.4309575170361471 |
| 467.21 | 469.69281643752356 | -2.4828164375235815 | -0.5608691138957983 |
| 472.59 | 468.5153727218602 | 4.074627278139758 | 0.9204597474090102 |
| 466.33 | 464.3095114085993 | 2.0204885914006923 | 0.45642908946816274 |
| 472.04 | 466.169002951847 | 5.870997048153015 | 1.3262603156304236 |
| 472.17 | 469.2063750462149 | 2.9636249537851427 | 0.6694839282628849 |
| 466.51 | 472.79218089209587 | -6.28218089209588 | -1.4191468918247268 |
| 460.8 | 469.8764663630868 | -9.076466363086809 | -2.0503769708593955 |
| 463.97 | 470.87346939701916 | -6.903469397019137 | -1.559496185458982 |
| 464.56 | 468.2319508045607 | -3.6719508045607085 | -0.8294949891974716 |
| 469.12 | 472.4449714286485 | -3.324971428648496 | -0.7511122251047269 |
| 471.26 | 467.1630433917525 | 4.096956608247467 | 0.9255039510005931 |
| 466.06 | 469.3130021437414 | -3.253002143741412 | -0.7348543381165886 |
| 470.66 | 470.0399558829108 | 0.6200441170892077 | 0.14006818598115695 |
| 463.65 | 464.0442884425802 | -0.3942884425802049 | -8.906989903365076e-2 |
| 470.35 | 467.7114787859095 | 2.638521214090531 | 0.5960428781510217 |
| 465.92 | 470.46126451393184 | -4.5412645139318215 | -1.02587326449146 |
| 466.67 | 467.51203531407947 | -0.8420353140794532 | -0.19021607612190924 |
| 471.38 | 469.4046202019984 | 1.9753797980015975 | 0.4462389970391413 |
| 468.45 | 470.40326389642064 | -1.9532638964206512 | -0.4412430070274576 |
| 467.22 | 469.66353144520883 | -2.443531445208805 | -0.5519946201974366 |
| 462.88 | 465.52654943877593 | -2.646549438775935 | -0.5978564569550795 |
| 473.56 | 467.6786715108734 | 5.881328489126588 | 1.3285941918109334 |
| 469.81 | 465.64964430715304 | 4.160355692846963 | 0.9398258266993826 |
| 468.66 | 467.8607823790049 | 0.7992176209951367 | 0.18054354406665346 |
| 468.35 | 467.26426723260613 | 1.0857327673938926 | 0.24526741726554308 |
| 469.94 | 467.0983914780623 | 2.841608521937701 | 0.6419203730291116 |
| 463.49 | 467.7236799517389 | -4.233679951738907 | -0.9563898027913122 |
| 465.48 | 467.5049711098421 | -2.024971109842056 | -0.4574416919740238 |
| 464.14 | 466.31353063126903 | -2.1735306312690454 | -0.49100134055869604 |
| 471.16 | 467.46352561567426 | 3.696474384325768 | 0.8350348745649541 |
| 472.67 | 467.6987016384138 | 4.971298361586207 | 1.1230180632103244 |
| 468.88 | 470.13332337428324 | -1.2533233742832408 | -0.2831261948065207 |
| 464.79 | 466.32771593378925 | -1.5377159337892294 | -0.34737057487344897 |
| 461.18 | 461.3756491943329 | -0.19564919433287287 | -4.4197222396898606e-2 |
| 468.82 | 467.03627043956425 | 1.783729560435745 | 0.4029451403943494 |
| 461.96 | 465.6730488393365 | -3.7130488393365226 | -0.8387790498308709 |
| 462.81 | 466.20636936169376 | -3.3963693616937576 | -0.76724104350462 |
| 465.62 | 471.8419294419814 | -6.221929441981388 | -1.405536067872571 |
| 464.79 | 467.025423832653 | -2.235423832652998 | -0.5049830367049549 |
| 465.39 | 467.36024219232854 | -1.9702421923285556 | -0.4450784100952837 |
| 465.51 | 470.36369995609516 | -4.85369995609517 | -1.0964525416975441 |
| 465.25 | 466.34250670048556 | -1.0925067004855578 | -0.24679765115367583 |
| 469.75 | 466.9286675356811 | 2.821332464318914 | 0.6373400044210754 |
| 468.64 | 466.5450197688411 | 2.0949802311588996 | 0.47325677731185334 |
| 456.71 | 469.52890927618625 | -12.818909276186275 | -2.895796152379444 |
| 468.87 | 464.17371789096717 | 4.696282109032836 | 1.0608917942097844 |
| 463.77 | 466.22172115408836 | -2.451721154088375 | -0.5538446783382351 |
| 464.16 | 468.9140947361635 | -4.754094736163495 | -1.0739516871848376 |
| 472.42 | 469.50273867747836 | 2.9172613225216537 | 0.6590103675152038 |
| 466.12 | 465.2396919188332 | 0.8803080811667883 | 0.19886190778235394 |
| 465.89 | 469.291500068156 | -3.401500068155997 | -0.7684000719143101 |
| 463.3 | 467.5233892738298 | -4.2233892738298096 | -0.9540651350015262 |
| 472.32 | 468.6339203654876 | 3.686079634512396 | 0.8326866968950778 |
| 472.88 | 468.23518412428797 | 4.644815875712027 | 1.049265553847453 |
| 472.52 | 467.40072495010907 | 5.119275049890916 | 1.1564460495859417 |
| 466.2 | 470.2308690130346 | -4.030869013034589 | -0.9105747398006214 |
| 464.32 | 470.06934793478035 | -5.74934793478036 | -1.298779737770393 |
| 471.52 | 468.2061275247934 | 3.313872475206608 | 0.7486049675853867 |
| 471.05 | 466.5674959516581 | 4.482504048341923 | 1.0125992544722646 |
| 465.0 | 467.4457105564226 | -2.4457105564226254 | -0.552486882193573 |
| 468.51 | 468.3292283291916 | 0.18077167080838308 | 4.083638455563342e-2 |
| 466.2 | 466.52892029567596 | -0.3289202956759709 | -7.43032114617912e-2 |
| 466.67 | 464.97984688167367 | 1.69015311832635 | 0.38180618893011364 |
| 458.19 | 469.62052738975217 | -11.430527389752172 | -2.5821601917725587 |
| 466.75 | 466.9234567199092 | -0.1734567199092112 | -3.9183934552884936e-2 |
| 467.83 | 471.0002382734735 | -3.170238273473501 | -0.7161579504665817 |
| 463.54 | 468.4888161194508 | -4.9488161194507825 | -1.1179393167374423 |
| 467.21 | 466.1266303832576 | 1.0833696167424023 | 0.24473358069515785 |
| 468.92 | 467.25704554531416 | 1.6629544546858597 | 0.37566200116631454 |
| 466.17 | 469.9193063400854 | -3.749306340085411 | -0.8469696321106422 |
| 473.56 | 469.33864000185486 | 4.221359998145147 | 0.9536067209045733 |
| 462.54 | 462.5353944778779 | 4.60552212211951e-3 | 1.040389080973342e-3 |
| 460.42 | 469.0357845125853 | -8.615784512585265 | -1.9463087774264596 |
| 463.1 | 465.4074674853422 | -2.3074674853421584 | -0.5212577234014426 |
| 469.35 | 469.2340241993221 | 0.11597580067791569 | 2.6198974509955283e-2 |
| 464.5 | 465.48772540492894 | -0.9877254049289377 | -0.22312751932133126 |
| 467.01 | 468.6698381136833 | -1.6598381136832927 | -0.3749580186284632 |
| 470.13 | 466.5182432985413 | 3.6117567014587166 | 0.8158971199557807 |
| 467.25 | 464.80335793821706 | 2.4466420617829385 | 0.5526973095848743 |
| 464.6 | 469.0300144538734 | -4.430014453873355 | -1.0007418364636667 |
| 464.56 | 468.0542535304745 | -3.4942535304745093 | -0.7893531119518733 |
| 470.44 | 466.2407858068176 | 4.199214193182399 | 0.948603975708334 |
| 464.23 | 466.68020136327283 | -2.450201363272811 | -0.5535013570539268 |
| 470.8 | 468.07562484757494 | 2.7243751524250683 | 0.615437313273332 |
| 470.19 | 468.4620083288529 | 1.7279916711470946 | 0.390353931433719 |
| 465.45 | 465.35539799683 | 9.46020031699959e-2 | 2.1370626071593804e-2 |
| 465.14 | 465.34301144661265 | -0.20301144661266335 | -4.58603577983067e-2 |
| 457.21 | 465.4373435562456 | -8.227343556245614 | -1.8585598276086635 |
| 462.69 | 470.2899851111997 | -7.5999851111997145 | -1.7168393323476852 |
| 460.66 | 465.705988879045 | -5.045988879044955 | -1.1398906775971094 |
| 462.64 | 464.775180629532 | -2.1351806295319875 | -0.4823380615634837 |
| 475.98 | 471.7241650123044 | 4.255834987695607 | 0.96139463327236 |
| 469.61 | 470.86762962520095 | -1.2576296252009342 | -0.28409897841626214 |
| 464.95 | 470.0085068177468 | -5.0585068177468315 | -1.1427184843899973 |
| 467.18 | 465.090966572103 | 2.089033427896993 | 0.47191339234564744 |
| 463.6 | 467.14100725665213 | -3.541007256652108 | -0.7999147952790192 |
| 462.77 | 465.7842034756254 | -3.014203475625436 | -0.6809096342868951 |
| 464.93 | 467.49881987016624 | -2.5688198701662373 | -0.5802973198353383 |
| 465.49 | 466.18608052784475 | -0.6960805278447424 | -0.15724483814107668 |
| 465.82 | 465.9570356485115 | -0.13703564851152805 | -3.095640160553223e-2 |
| 461.52 | 465.6600911572598 | -4.140091157259803 | -0.935248060922378 |
| 460.54 | 461.72665249570616 | -1.1866524957061415 | -0.2680652196876797 |
| 460.54 | 466.6642858393167 | -6.124285839316656 | -1.3834783433962559 |
| 463.22 | 462.1388114830899 | 1.0811885169101174 | 0.24424086946940488 |
| 467.32 | 467.1954080636746 | 0.12459193632537335 | 2.8145362608873965e-2 |
| 467.66 | 467.57038570428426 | 8.961429571576218e-2 | 2.0243901188534075e-2 |
| 470.71 | 466.8779893764293 | 3.8320106235706817 | 0.8656525590853151 |
| 466.34 | 468.28371557585353 | -1.9437155758535596 | -0.43908603802454604 |
| 464.0 | 465.49312878230023 | -1.4931287823002322 | -0.3372983215369459 |
| 454.16 | 460.9202947940051 | -6.760294794005063 | -1.527152991853827 |
| 460.19 | 464.1520666190232 | -3.962066619023176 | -0.8950322545891167 |
| 467.71 | 465.5258417359535 | 2.1841582640464594 | 0.49340212657275334 |
| 465.01 | 464.28156360850716 | 0.7284363914928349 | 0.16455403921586076 |
| 462.87 | 464.1635216732549 | -1.2935216732548724 | -0.2922070048026174 |
| 466.73 | 466.3736543730528 | 0.35634562694724536 | 8.049860352378384e-2 |
| 469.34 | 467.08552274770574 | 2.2544772522942367 | 0.5092872109601911 |
| 458.99 | 465.25377872135545 | -6.263778721355436 | -1.4149898349271792 |
| 465.72 | 464.56900508273293 | 1.1509949172670986 | 0.26001016007049604 |
| 465.63 | 461.87533959682145 | 3.7546604031785478 | 0.848179116862477 |
| 471.24 | 464.4149735638803 | 6.8250264361196855 | 1.541775892767944 |
| 456.41 | 468.5317634787664 | -12.121763478766354 | -2.7383106694634813 |
| 460.51 | 466.42415027580006 | -5.914150275800068 | -1.3360086450624162 |
| 467.77 | 466.6719213555882 | 1.0980786444117712 | 0.24805635526298545 |
| 456.63 | 464.60786745115865 | -7.977867451158659 | -1.8022030869799206 |
| 468.02 | 467.544960954765 | 0.4750390452350075 | 0.10731148881574702 |
| 463.93 | 466.26419991369926 | -2.3341999136992513 | -0.5272965884493478 |
| 463.12 | 465.43441550001705 | -2.3144155000170485 | -0.522827282380973 |
| 460.06 | 467.4928608009505 | -7.432860800950493 | -1.6790858913831084 |
| 461.6 | 466.41675498345813 | -4.816754983458111 | -1.0881066592743878 |
| 463.27 | 466.61302775183003 | -3.34302775183005 | -0.7551911549160485 |
| 467.82 | 462.99098636012917 | 4.829013639870823 | 1.0908758941061858 |
| 462.59 | 464.3870766666383 | -1.7970766666383042 | -0.40596025641974887 |
| 460.15 | 465.9823776729848 | -5.832377672984819 | -1.3175361850815699 |
| 463.1 | 464.5934173732165 | -1.4934173732164595 | -0.33736351432728534 |
| 461.6 | 464.9855482511964 | -3.385548251196383 | -0.7647965508049966 |
| 462.64 | 466.6093718548404 | -3.9693718548404036 | -0.8966825099514081 |
| 460.56 | 459.9584434481331 | 0.6015565518669064 | 0.13589183843986818 |
| 459.85 | 461.87302187524506 | -2.0230218752450355 | -0.45700135918716 |
| 465.99 | 465.2019993258076 | 0.7880006741924035 | 0.17800963180524587 |
| 464.49 | 465.03190605144545 | -0.5419060514454372 | -0.12241676923652416 |
| 468.62 | 464.51031407803373 | 4.109685921966275 | 0.928379507484781 |
| 458.26 | 464.7510595901315 | -6.491059590131499 | -1.4663326638006033 |
| 466.5 | 462.10563966655616 | 4.394360333443842 | 0.9926875580120624 |
| 459.31 | 463.52420449023833 | -4.21420449023833 | -0.9519902891303528 |
| 459.77 | 465.12795484714036 | -5.357954847140377 | -1.2103639004447255 |
| 457.1 | 458.39794118753656 | -1.2979411875365372 | -0.29320537464645474 |
| 469.18 | 464.49240158564226 | 4.687598414357751 | 1.0589301445025614 |
| 472.28 | 463.4938095964465 | 8.786190403553462 | 1.9848035286394352 |
| 459.15 | 464.74092024201923 | -5.590920242019251 | -1.262990865781086 |
| 466.63 | 468.69706628494185 | -2.067066284941859 | -0.4669510069602996 |
| 462.69 | 464.01147313398934 | -1.3214731339893433 | -0.29852124969696514 |
| 468.53 | 466.52540885533836 | 2.0045911446616174 | 0.4528378506109543 |
| 453.99 | 463.6262300043777 | -9.636230004377694 | -2.176827775976124 |
| 471.97 | 469.18664060318713 | 2.783359396812898 | 0.6287618750023547 |
| 457.74 | 458.7253721171194 | -0.9853721171193683 | -0.22259591077043436 |
| 466.83 | 462.85471321992253 | 3.975286780077454 | 0.8980186936604918 |
| 466.2 | 469.16650047432273 | -2.9665004743227428 | -0.6701335093722809 |
| 465.14 | 464.90298984127037 | 0.23701015872961761 | 5.3540678924927795e-2 |
| 457.36 | 466.2852656032666 | -8.92526560326661 | -2.0162206655848456 |
| 439.21 | 465.39633546049805 | -26.18633546049807 | -5.915502468864325 |
| 462.59 | 462.31339644999167 | 0.2766035500083035 | 6.248484005862629e-2 |
| 457.3 | 457.58467899039783 | -0.28467899039782196 | -6.430908490699098e-2 |
| 463.57 | 462.46440155189674 | 1.105598448103251 | 0.24975508158417795 |
| 462.09 | 461.6436673101792 | 0.44633268982079244 | 0.10082671294548266 |
| 465.41 | 465.13586499514474 | 0.27413500485528175 | 6.192719483296151e-2 |
| 464.17 | 468.0777122780922 | -3.9077122780921627 | -0.8827535896932235 |
| 464.59 | 463.94240853336476 | 0.6475914666352196 | 0.14629114201469284 |
| 464.38 | 462.7655254273002 | 1.6144745726998053 | 0.3647103786297681 |
| 464.95 | 464.3512532840681 | 0.5987467159318953 | 0.13525709550549558 |
| 455.15 | 458.1774466179169 | -3.027446617916951 | -0.6839012648279135 |
| 456.91 | 458.36321179088014 | -1.4532117908801183 | -0.3282810590165173 |
| 456.21 | 467.4754642409999 | -11.265464240999904 | -2.5448723679212413 |
| 467.3 | 462.93565136830296 | 4.364348631697055 | 0.9859079039422615 |
| 464.72 | 460.79339608207107 | 3.926603917928958 | 0.8870212178332524 |
| 462.58 | 466.71318747004864 | -4.133187470048654 | -0.9336885155326297 |
| 466.6 | 469.2173130099923 | -2.6173130099923014 | -0.5912519373226456 |
| 459.42 | 464.720482732063 | -5.30048273206296 | -1.19738092926328 |
| 464.14 | 464.335595847906 | -0.19559584790602003 | -4.418517142015048e-2 |
| 460.45 | 465.0880608446689 | -4.638060844668928 | -1.0477395899387958 |
| 468.91 | 465.0497057218854 | 3.8602942781146226 | 0.8720418466790518 |
| 466.39 | 467.13452750857033 | -0.7445275085703429 | -0.16818902827121646 |
| 467.22 | 463.63133669846115 | 3.5886633015388725 | 0.8106803127226195 |
| 462.77 | 462.43127985662505 | 0.3387201433749283 | 7.651700053300835e-2 |
| 464.95 | 462.0817954663473 | 2.8682045336527153 | 0.6479284215091841 |
| 457.12 | 462.7228887148107 | -5.602888714810717 | -1.2656945480299158 |
| 461.5 | 463.2389898882613 | -1.738989888261301 | -0.3928384325809046 |
| 458.46 | 465.7322155460913 | -7.272215546091331 | -1.6427960713292782 |
| 465.89 | 465.9391561803266 | -4.91561803266336e-2 | -1.1104398571567456e-2 |
| 466.15 | 464.67588000342283 | 1.4741199965771443 | 0.33300423009965163 |
| 455.48 | 462.03627301808893 | -6.556273018088916 | -1.4810644003074978 |
| 464.95 | 468.46315966006046 | -3.5131596600604666 | -0.7936240133313774 |
| 454.88 | 456.7218449478403 | -1.8418449478402863 | -0.41607342702266226 |
| 457.41 | 460.9973530979208 | -3.5873530979207544 | -0.8103843372605553 |
| 468.88 | 468.60583110746245 | 0.2741688925375456 | 6.1934850072404936e-2 |
| 461.86 | 468.0452621538944 | -6.185262153894371 | -1.3972529144877643 |
| 456.08 | 461.16748971043216 | -5.08748971043218 | -1.1492657301278992 |
| 462.94 | 462.09664222758136 | 0.8433577724186421 | 0.19051481992981023 |
| 460.87 | 460.8634611992421 | 6.538800757880381e-3 | 1.4771174105289492e-3 |
| 461.06 | 464.39198726436115 | -3.331987264361146 | -0.7526971048807434 |
| 454.94 | 458.02055270119473 | -3.0805527011947333 | -0.6958979478771827 |
| 466.22 | 464.5123542802937 | 1.7076457197063064 | 0.3857577737865013 |
| 459.95 | 461.1346442030387 | -1.1846442030387152 | -0.26761154566176326 |
| 463.47 | 463.18977819659995 | 0.2802218034000816 | 6.330220478322962e-2 |
| 456.58 | 464.6775725663142 | -8.097572566314227 | -1.8292445149531718 |
| 463.57 | 465.5402356742791 | -1.9702356742790812 | -0.4450769376655955 |
| 462.44 | 462.1000323229214 | 0.33996767707861864 | 7.679881883917433e-2 |
| 448.59 | 459.28384302135794 | -10.693843021357964 | -2.4157429316491066 |
| 462.5 | 464.0520812837949 | -1.5520812837949052 | -0.35061571253514523 |
| 463.76 | 462.0021066209579 | 1.757893379042116 | 0.39710873785339107 |
| 459.48 | 464.6824431291315 | -5.202443129131495 | -1.1752337481862478 |
| 455.05 | 457.21309738475657 | -2.163097384756554 | -0.488644466470834 |
| 456.11 | 461.3420214113817 | -5.232021411381709 | -1.1819154926380382 |
| 472.17 | 467.91529134370023 | 4.254708656299783 | 0.9611401946105317 |
| 461.54 | 465.4513233379416 | -3.9113233379415533 | -0.88356932939405 |
| 459.69 | 461.3200136826322 | -1.6300136826321818 | -0.3682206690752407 |
| 461.54 | 459.19347653551756 | 2.346523464482459 | 0.5300804829424589 |
| 462.48 | 459.1522349051031 | 3.327765094896904 | 0.7517433153011605 |
| 442.48 | 460.6299621913311 | -18.1499621913311 | -4.100082896844213 |
| 465.14 | 465.94867946942674 | -0.8086794694267496 | -0.18268097898348856 |
| 464.62 | 462.5145658398506 | 2.1054341601494 | 0.4756183235788339 |
| 465.78 | 463.3496922983309 | 2.430307701669051 | 0.5490073718413218 |
| 462.52 | 461.99087118644087 | 0.5291288135591117 | 0.11953038666589516 |
| 454.78 | 457.1797966088055 | -2.3997966088055023 | -0.5421149051411095 |
| 465.03 | 464.9190479188059 | 0.11095208119405697 | 2.5064114496629736e-2 |
| 463.74 | 462.72099107520137 | 1.0190089247986407 | 0.230194477556215 |
| 459.59 | 461.9941154580848 | -2.4041154580848456 | -0.5430905347252163 |
| 465.6 | 458.7011562788462 | 6.89884372115381 | 1.5584512436402695 |
| 464.7 | 461.4647200415145 | 3.235279958485478 | 0.7308508901811838 |
| 460.54 | 462.40970062782714 | -1.8697006278271147 | -0.4223660350121439 |
| 465.52 | 462.47440172010425 | 3.045598279895728 | 0.6880017317073447 |
| 465.64 | 464.23959443772316 | 1.4004055622768306 | 0.3163521132445753 |
| 468.22 | 468.12040219816635 | 9.959780183368139e-2 | 2.2499178761738656e-2 |
| 464.2 | 461.97170222353844 | 2.2282977764615453 | 0.5033732580836815 |
| 472.63 | 464.18342103476823 | 8.446578965231765 | 1.908085184261808 |
| 460.31 | 460.4941449517029 | -0.18414495170287637 | -4.159840990620793e-2 |
| 458.25 | 461.5472235985969 | -3.2972235985968723 | -0.7448439804538625 |
| 459.25 | 462.26646301676067 | -3.0164630167606674 | -0.681420065430807 |
| 463.62 | 462.2293585659873 | 1.3906414340127071 | 0.3141463932063569 |
| 457.26 | 464.22591401634116 | -6.9659140163411735 | -1.573602447113043 |
| 460.0 | 459.5632798612247 | 0.4367201387752857 | 9.865523425471445e-2 |
| 460.25 | 461.3519558174483 | -1.1019558174482995 | -0.24893221002717708 |
| 464.6 | 460.4994805562497 | 4.100519443750329 | 0.9263087968044336 |
| 461.49 | 463.40687222781077 | -1.9168722278107566 | -0.43302211618029557 |
| 449.98 | 456.35386691539543 | -6.373866915395411 | -1.4398587970092795 |
| 464.72 | 462.67093183857946 | 2.049068161420564 | 0.462885224472874 |
| 460.08 | 461.81109623075827 | -1.7310962307582827 | -0.391055252551079 |
| 454.19 | 460.0644340540906 | -5.874434054090614 | -1.3270367364908389 |
| 464.27 | 462.014274652855 | 2.25572534714496 | 0.5095691560296709 |
| 456.55 | 460.5836092644097 | -4.033609264409677 | -0.9111937635582255 |
| 464.5 | 463.360199769915 | 1.1398002300849726 | 0.25748127626527917 |
| 468.8 | 464.34868588625807 | 4.451314113741944 | 1.0055534371830066 |
| 465.16 | 461.6467454037935 | 3.5132545962065365 | 0.7936454594404486 |
| 463.65 | 462.4960995942487 | 1.1539004057513012 | 0.26066651094965915 |
| 466.36 | 460.43082906265056 | 5.929170937349454 | 1.3394018178342768 |
| 455.53 | 462.1757595182412 | -6.645759518241221 | -1.5012794324329175 |
| 454.06 | 459.4288341311474 | -5.368834131147423 | -1.212821534560818 |
| 453.0 | 454.3258801823864 | -1.3258801823864133 | -0.2995168034930508 |
| 461.47 | 460.83972369537236 | 0.6302763046276709 | 0.14237964077547013 |
| 457.67 | 459.652078633235 | -1.9820786332350053 | -0.44775226629445647 |
| 458.67 | 457.84281119185783 | 0.8271888081421821 | 0.18686224516459876 |
| 461.01 | 462.29977029786545 | -1.2897702978654593 | -0.2913595677715297 |
| 458.34 | 458.99629100134234 | -0.6562910013423675 | -0.14825636999077735 |
| 457.01 | 456.94689658887796 | 6.310341112202877e-2 | 1.4255082955353757e-2 |
| 455.75 | 455.92052796835753 | -0.1705279683575327 | -3.85223285385276e-2 |
| 456.62 | 455.0999707958263 | 1.5200292041736816 | 0.34337513637978356 |
| 457.67 | 463.6885973525347 | -6.018597352534698 | -1.3596032767443251 |
| 456.16 | 456.72206228080466 | -0.5620622808046392 | -0.12697006859821677 |
| 455.97 | 460.34419741616995 | -4.37419741616992 | -0.9881327478480624 |
| 457.05 | 457.5432003190509 | -0.49320031905091355 | -0.11141412701259497 |
| 457.71 | 456.69285780084897 | 1.0171421991510101 | 0.22977278357028447 |
| 459.43 | 457.13828420727975 | 2.291715792720254 | 0.5176994104510022 |
| 457.98 | 457.7082041695146 | 0.27179583048541645 | 6.13987745130998e-2 |
| 459.13 | 460.61769921749 | -1.4876992174899897 | -0.3360717809204362 |
| 457.35 | 460.2397779497155 | -2.8897779497154943 | -0.6528018638498932 |
| 456.56 | 454.03599822567077 | 2.5240017743292356 | 0.5701728960887047 |
| 454.57 | 457.06546864918414 | -2.495468649184147 | -0.5637272529976811 |
| 451.9 | 457.7462919446442 | -5.846291944644236 | -1.3206794239848478 |
| 457.33 | 456.68265807481106 | 0.6473419251889254 | 0.1462347705134622 |
| 467.59 | 461.5754309315541 | 6.014569068445894 | 1.358693285939825 |
| 463.99 | 464.77705443177615 | -0.7870544317761414 | -0.17779587530777846 |
| 452.55 | 459.8014970847607 | -7.251497084760672 | -1.6381157635658086 |
| 452.28 | 454.94641693932414 | -2.666416939324165 | -0.602344532375961 |
| 453.55 | 454.8958623057626 | -1.34586230576258 | -0.3040307722514201 |
| 448.88 | 452.5071708494883 | -3.627170849488323 | -0.8193791814630933 |
| 448.71 | 455.24182428555105 | -6.5318242855510675 | -1.4755414229551909 |
| 454.59 | 459.58273190303845 | -4.992731903038475 | -1.1278599078269054 |
| 451.14 | 453.10756062981807 | -1.967560629818081 | -0.4444726441730127 |
| 458.01 | 456.42171751548034 | 1.5882824845196524 | 0.35879357661949324 |
| 456.55 | 456.5005129659639 | 4.948703403613308e-2 | 1.1179138541897764e-2 |
| 458.04 | 456.4920988999565 | 1.547901100043532 | 0.34967140754299175 |
| 463.31 | 463.61908166044077 | -0.3090816604407678 | -6.982165672536109e-2 |
| 454.34 | 454.4243094498896 | -8.430944988964484e-2 | -1.904553463477631e-2 |
| 454.29 | 454.14166454199494 | 0.14833545800507864 | 3.3509032578186985e-2 |
| 455.82 | 453.8961915229473 | 1.9238084770527166 | 0.43458901734435373 |
| 451.44 | 453.6649941349345 | -2.224994134934491 | -0.5026269642909126 |
| 442.45 | 449.9074433355401 | -7.45744333554012 | -1.684639094666372 |
| 454.98 | 459.22851589459475 | -4.248515894594732 | -0.9597412475447774 |
| 465.26 | 459.05202239898534 | 6.2079776010146475 | 1.4023843420494426 |
| 451.06 | 452.94903640134396 | -1.889036401343958 | -0.4267339931080307 |
| 452.77 | 453.1802042498492 | -0.41020424984924375 | -9.266528554109059e-2 |
| 463.47 | 461.3678096135659 | 2.102190386434131 | 0.47488555394598253 |
| 461.73 | 460.89674705500823 | 0.8332529449917843 | 0.18823213582988355 |
| 467.54 | 463.8188758742396 | 3.7211241257604115 | 0.8406032598983453 |
| 454.74 | 459.1270333631719 | -4.387033363171895 | -0.9910323928287407 |
| 451.04 | 455.213182837326 | -4.173182837325953 | -0.9427234831869965 |
| 464.13 | 457.4956830813669 | 6.63431691863309 | 1.4986945451227311 |
| 456.25 | 452.8810496638165 | 3.3689503361835023 | 0.7610470759162783 |
| 457.43 | 456.48090497858726 | 0.9490950214127452 | 0.21440090198276052 |
| 448.17 | 456.5591352486571 | -8.389135248657112 | -1.8951086283122098 |
| 452.93 | 453.45921998591575 | -0.5292199859157449 | -0.1195509825335976 |
| 455.24 | 454.7581350474279 | 0.4818649525720957 | 0.10885346370435622 |
| 454.44 | 461.43742015467615 | -6.997420154676149 | -1.580719694938195 |
| 460.27 | 463.53345887643894 | -3.2634588764389605 | -0.7372165177419857 |
| 450.5 | 452.02894677235804 | -1.52894677235804 | -0.3453896181957068 |
| 455.22 | 455.963340998892 | -0.7433409988919948 | -0.1679209953140206 |
| 457.17 | 456.69115708185933 | 0.47884291814068547 | 0.10817078505437988 |
| 458.47 | 455.70816977608035 | 2.7618302239196737 | 0.623898427209341 |
| 456.49 | 455.5784197907349 | 0.9115802092650824 | 0.2059262926120357 |
| 451.29 | 457.8698842565306 | -6.57988425653059 | -1.4863981874464185 |
| 453.78 | 454.0602820488982 | -0.2802820488982434 | -6.331581426263354e-2 |
| 463.2 | 460.7479119618785 | 2.452088038121474 | 0.5539275575714465 |
| 452.1 | 453.92151217696727 | -1.8215121769672464 | -0.4114802468703741 |
| 451.93 | 453.0532072948905 | -1.1232072948905056 | -0.2537329263193067 |
| 450.55 | 454.758298968509 | -4.208298968508984 | -0.9506562296769948 |
| 461.49 | 456.7605922899199 | 4.729407710080125 | 1.068374879236074 |
| 452.52 | 454.0242328275001 | -1.5042328275001182 | -0.3398067292862239 |
| 451.23 | 452.6641989591518 | -1.4341989591517859 | -0.32398605358520316 |
| 455.44 | 457.32803096446935 | -1.8880309644693511 | -0.42650686456142684 |
| 440.64 | 450.3190565318654 | -9.679056531865399 | -2.186502303726239 |
| 467.51 | 461.3397464375879 | 6.170253562412086 | 1.3938624683483432 |
| 452.37 | 456.8872770659861 | -4.517277065986093 | -1.0204544914921314 |
| 462.05 | 460.8339970164602 | 1.216002983539795 | 0.27469550529997006 |
| 464.48 | 462.10615685280686 | 2.3738431471931563 | 0.5362520089571434 |
| 453.79 | 457.0494808979769 | -3.2594808979768572 | -0.7363178909964009 |
| 449.55 | 452.3356980438659 | -2.785698043865864 | -0.6292901762011418 |
| 459.45 | 455.30258286482143 | 4.147417135178557 | 0.9369030019327613 |
| 463.02 | 462.7299869900826 | 0.2900130099174021 | 6.551404180844994e-2 |
| 455.79 | 455.2223463598715 | 0.5676536401285261 | 0.1282331586527475 |
| 453.25 | 453.31727625144936 | -6.7276251449357e-2 | -1.5197729065410778e-2 |
| 454.71 | 459.55748654713716 | -4.84748654713718 | -1.0950489304100275 |
| 450.74 | 456.7379403460756 | -5.997940346075609 | -1.3549368516581415 |
| 453.9 | 456.5169354267787 | -2.6169354267786957 | -0.5911666411407626 |
| 450.87 | 451.9912034594031 | -1.121203459403091 | -0.2532802591719359 |
| 453.28 | 457.39523074964615 | -4.115230749646173 | -0.9296320860244299 |
| 454.47 | 454.91390716792046 | -0.44390716792042895 | -0.1002787866878538 |
| 446.7 | 453.70377279073705 | -7.0037727907370595 | -1.5821547576776454 |
| 454.58 | 458.80810089986693 | -4.228100899866945 | -0.9551294929945369 |
| 458.64 | 459.2317295422404 | -0.5917295422403868 | -0.13367191347958032 |
| 454.02 | 452.8313052555021 | 1.188694744497866 | 0.2685265644141194 |
| 455.88 | 453.2048261109298 | 2.6751738890702086 | 0.6043227304296972 |
| 460.19 | 460.78946144208953 | -0.5994614420895346 | -0.13541855239802086 |
| 457.58 | 456.60185019595883 | 0.978149804041152 | 0.22096438768429855 |
| 459.68 | 456.60000256329795 | 3.079997436702058 | 0.6957725134313342 |
| 454.6 | 461.5337919917975 | -6.933791991797477 | -1.566346070374885 |
| 457.55 | 455.08314377928764 | 2.4668562207123728 | 0.5572636952570301 |
| 455.42 | 454.7344713102365 | 0.6855286897635438 | 0.15486117417577897 |
| 455.76 | 454.4253732327452 | 1.3346267672548038 | 0.3014926601172488 |
| 447.47 | 450.84382297953874 | -3.3738229795387156 | -0.7621478077785558 |
| 455.7 | 454.2509501800389 | 1.4490498199610897 | 0.32734086830965053 |
| 455.95 | 463.1377560912732 | -7.187756091273229 | -1.6237166505279563 |
| 451.05 | 455.0606464477159 | -4.010646447715885 | -0.9060064551221478 |
| 449.89 | 452.78710305999687 | -2.89710305999688 | -0.6544566088606873 |
| 451.49 | 452.43308379405045 | -0.9430837940504375 | -0.21304296358942265 |
| 456.04 | 453.7206916287245 | 2.319308371275497 | 0.5239325837337663 |
| 458.06 | 453.97719041615505 | 4.0828095838449485 | 0.9223081331700905 |
| 448.17 | 452.20842313451385 | -4.038423134513835 | -0.9122812185222428 |
| 451.8 | 452.90074763749135 | -1.100747637491338 | -0.24865928174635601 |
| 465.66 | 461.8378158906793 | 3.8221841093207445 | 0.8634327460307729 |
| 449.26 | 452.41543202652065 | -3.155432026520657 | -0.7128132140281332 |
| 451.08 | 457.75864371455697 | -6.6786437145569835 | -1.5087079840447288 |
| 467.72 | 461.3187533462041 | 6.4012466537959085 | 1.4460438896255918 |
| 463.35 | 462.37131638295244 | 0.9786836170475794 | 0.22108497623179488 |
| 453.47 | 454.29376940501464 | -0.823769405014616 | -0.18608980078521345 |
| 450.88 | 453.15381920341724 | -2.273819203417247 | -0.5136565645795023 |
| 456.08 | 457.3375291656138 | -1.2575291656137892 | -0.2840762845598929 |
| 448.04 | 451.78495851322157 | -3.7449585132215475 | -0.8459874564799138 |
| 450.17 | 451.8524216632198 | -1.6824216632197704 | -0.380059650479177 |
| 450.54 | 455.69553323527913 | -5.155533235279108 | -1.1646367865259744 |
| 456.61 | 453.9716415466405 | 2.6383584533595013 | 0.5960061104441506 |
| 448.73 | 447.41632457686086 | 1.3136754231391592 | 0.29675974405004724 |
| 456.57 | 456.66826631159057 | -9.826631159057797e-2 | -2.2198394643539735e-2 |
| 456.06 | 451.3868160236597 | 4.673183976340283 | 1.0556739178415897 |
| 457.41 | 455.8596185860852 | 1.5503814139148062 | 0.35023171132628417 |
| 446.29 | 451.60844251280884 | -5.318442512808815 | -1.2014380501041335 |
| 453.84 | 451.542577765937 | 2.297422234062992 | 0.5189884975744112 |
| 456.03 | 454.3347436794228 | 1.6952563205771867 | 0.38295900412869216 |
| 461.16 | 456.98501253896984 | 4.1749874610301845 | 0.9431311483218855 |
| 450.5 | 450.35966629930124 | 0.14033370069876128 | 3.170143276445295e-2 |
| 459.12 | 453.2290277149437 | 5.890972285056307 | 1.3307727287321278 |
| 444.87 | 450.8892362675085 | -6.019236267508518 | -1.3597476078635353 |
| 468.27 | 460.77739524450277 | 7.4926047554972115 | 1.6925820719064946 |
| 452.04 | 451.5662781980391 | 0.4737218019609486 | 0.10701392309290869 |
| 456.89 | 455.22151625901614 | 1.668483740983845 | 0.3769110689022767 |
| 456.55 | 458.86327155628703 | -2.3132715562870203 | -0.5225688650866008 |
| 455.07 | 454.4962025118092 | 0.5737974881907917 | 0.12962105610924668 |
| 454.94 | 452.9973261711798 | 1.9426738288202046 | 0.4388507070002017 |
| 444.64 | 451.56134671760844 | -6.921346717608458 | -1.5635346785212256 |
| 464.54 | 461.26403285215525 | 3.275967147844767 | 0.7400421406892627 |
| 454.28 | 454.96273138933435 | -0.6827313893343785 | -0.1542292630166233 |
| 450.26 | 450.45712572408434 | -0.19712572408434426 | -4.453077099148361e-2 |
| 450.44 | 452.7340333117575 | -2.2940333117575165 | -0.5182229388235478 |
| 449.23 | 451.3669335966618 | -2.136933596661777 | -0.48273405745988357 |
| 452.41 | 450.86300710254875 | 1.5469928974512754 | 0.34946624425526 |
| 451.75 | 450.87541624186673 | 0.8745837581332694 | 0.1975687811786549 |
| 449.66 | 451.1697942873412 | -1.5097942873411512 | -0.34106306503697253 |
| 446.03 | 450.6475445784032 | -4.617544578403226 | -1.0431049581122194 |
| 456.36 | 453.93684539976664 | 2.4231546002333744 | 0.5473914836897852 |
| 451.22 | 452.6438110022995 | -1.4238110022994874 | -0.32163941044764294 |
| 440.03 | 449.38085095551605 | -9.35085095551608 | -2.1123605476138505 |
| 462.86 | 459.86949886435633 | 2.990501135643683 | 0.6755552672777567 |
| 451.14 | 454.5790164502554 | -3.4390164502553944 | -0.7768750359376887 |
| 456.03 | 455.7041227162542 | 0.32587728374579683 | 7.361579398740335e-2 |
| 446.35 | 451.5591661954991 | -5.209166195499051 | -1.1767524912633618 |
| 448.85 | 451.4495789708702 | -2.5995789708701977 | -0.5872458115946685 |
| 448.71 | 449.41092940189 | -0.7009294018899936 | -0.15834020050780284 |
| 459.39 | 456.368436088565 | 3.0215639114350097 | 0.682572359347037 |
| 456.53 | 455.3814815227917 | 1.1485184772082562 | 0.25945073138280605 |
| 455.58 | 454.77441817350825 | 0.805581826491732 | 0.18198122034572886 |
| 459.47 | 455.56852272126105 | 3.90147727873898 | 0.8813451011277746 |
| 453.8 | 453.3887778929569 | 0.4112221070431019 | 9.289521984221884e-2 |
| 447.1 | 450.7408294300028 | -3.640829430002782 | -0.8224646596460314 |
| 446.57 | 451.436105216529 | -4.866105216528979 | -1.0992548944297087 |
| 455.28 | 454.1835978057384 | 1.0964021942616 | 0.24767764457942207 |
| 453.96 | 461.1739549532308 | -7.213954953230825 | -1.6296349827369583 |
| 454.5 | 450.58677338372456 | 3.9132266162754377 | 0.8839992806447705 |
| 449.31 | 450.2319334353776 | -0.9219334353775821 | -0.20826509006315633 |
| 449.63 | 454.6358406173096 | -5.005840617309616 | -1.1308211710304394 |
| 448.92 | 452.85108866192866 | -3.9310886619286407 | -0.888034323093527 |
| 457.14 | 451.05259673451775 | 6.087403265482237 | 1.375146556884749 |
| 450.98 | 451.5232844413466 | -0.5432844413466 | -0.12272814800411408 |
| 452.67 | 450.58585768893414 | 2.084142311065875 | 0.4708084873186104 |
| 449.03 | 451.0815006280822 | -2.0515006280822377 | -0.4634347195545461 |
| 453.33 | 451.80697350389795 | 1.5230264961020339 | 0.3440522256895429 |
| 455.13 | 453.19662660647845 | 1.9333733935215491 | 0.4367497353673552 |
| 454.36 | 455.59739505823774 | -1.2373950582377233 | -0.2795279825620374 |
| 454.84 | 451.430922332517 | 3.4090776674829613 | 0.7701118542900588 |
| 454.23 | 454.4162557726164 | -0.18625577261639137 | -4.2075245099282746e-2 |
| 447.06 | 447.51848167260005 | -0.458481672600044 | -0.10357117246458049 |
| 457.57 | 457.4899833309971 | 8.001666900287319e-2 | 1.80757939097979e-2 |
| 455.49 | 454.7734968589603 | 0.7165031410397091 | 0.16185831370575537 |
| 462.44 | 459.9896954813451 | 2.450304518654889 | 0.5535246598913276 |
| 451.59 | 453.5274885789799 | -1.9374885789799237 | -0.4376793572220653 |
| 452.45 | 451.13958592197287 | 1.3104140780271223 | 0.2960230050324334 |
| 448.79 | 451.01129177115814 | -2.2212917711581213 | -0.501790599000596 |
| 450.25 | 452.16097295111786 | -1.9109729511178557 | -0.4316894674828857 |
| 456.11 | 455.0355456385039 | 1.074454361496123 | 0.24271962137276715 |
| 445.58 | 448.2416124360999 | -2.661612436099915 | -0.6012591933934375 |
| 452.96 | 450.748723139911 | 2.211276860088958 | 0.499528227037766 |
| 452.94 | 453.9480760674323 | -1.0080760674322846 | -0.227724741199252 |
| 452.39 | 459.1480198621134 | -6.758019862113429 | -1.5266390839325845 |
| 445.96 | 448.54249260358677 | -2.582492603586786 | -0.5833859951647822 |
| 452.8 | 450.84813038147 | 1.9518696185299973 | 0.44092803915740614 |
| 458.97 | 452.9523382793844 | 6.017661720615649 | 1.3593919171619417 |
| 460.1 | 457.2797775931854 | 2.8202224068146506 | 0.637089242037123 |
| 453.83 | 450.67534900317014 | 3.1546509968298437 | 0.712636779143946 |
| 443.76 | 448.7439698617689 | -4.983969861768912 | -1.1258805595962003 |
| 450.46 | 449.81767655065505 | 0.6423234493449286 | 0.1451010950402414 |
| 446.53 | 449.46273191454395 | -2.9327319145439787 | -0.6625051797404924 |
| 446.34 | 450.23276134284185 | -3.8927613428418795 | -0.8793761681170623 |
| 449.72 | 448.3011628860887 | 1.4188371139113087 | 0.3205158072965067 |
| 447.14 | 446.65132022669536 | 0.4886797733046251 | 0.11039293412506312 |
| 455.5 | 459.6412858596406 | -4.141285859640618 | -0.935517944613967 |
| 448.22 | 449.8084139802319 | -1.5884139802318487 | -0.35882328154752013 |
| 447.84 | 450.0492245621943 | -2.209224562194322 | -0.4990646122154142 |
| 447.58 | 450.1534891767275 | -2.5734891767274917 | -0.5813521178437366 |
| 447.06 | 447.2352893702614 | -0.17528937026139602 | -3.959793091748769e-2 |
| 447.69 | 452.1889854808296 | -4.498985480829617 | -1.016322415917227 |
| 462.01 | 457.2763644769643 | 4.733635523035673 | 1.069329943682345 |
| 448.92 | 449.22543586447597 | -0.30543586447595317 | -6.899807012373943e-2 |
| 442.82 | 446.9636998742384 | -4.1436998742383935 | -0.9360632713678595 |
| 453.46 | 456.1194901357003 | -2.6594901357003096 | -0.600779764980383 |
| 446.2 | 450.6991034809647 | -4.49910348096472 | -1.0163490721896018 |
| 443.77 | 447.0742816761689 | -3.3042816761688982 | -0.7464384026808953 |
| 448.9 | 447.93156732156075 | 0.9684326784392283 | 0.21876928556414296 |
| 460.6 | 459.53962876300176 | 1.0603712369982645 | 0.23953823855339262 |
| 444.44 | 450.41539217977487 | -5.975392179774872 | -1.349843212892932 |
| 445.95 | 450.2091683575852 | -4.259168357585224 | -0.9621476427127764 |
| 453.18 | 450.5270199129538 | 2.652980087046217 | 0.599309142680298 |
| 443.46 | 446.7475520504839 | -3.287552050483896 | -0.7426591743046266 |
| 444.64 | 444.5071996193093 | 0.13280038069069633 | 2.999965310254963e-2 |
| 465.57 | 459.3265106832487 | 6.2434893167512655 | 1.4104064512304106 |
| 446.41 | 447.6568115900025 | -1.246811590002494 | -0.281655180427759 |
| 450.39 | 454.7627521673882 | -4.372752167388228 | -0.9878062656356458 |
| 452.38 | 454.2185133216816 | -1.838513321681603 | -0.4153208114916822 |
| 445.66 | 445.39798853968557 | 0.2620114603144543 | 5.918848182094669e-2 |
| 444.31 | 446.5197598700026 | -2.2097598700025856 | -0.4991855384391755 |
| 450.95 | 449.2875712413884 | 1.662428758611611 | 0.37554324623667645 |
| 442.02 | 446.61697690688305 | -4.5969769068830715 | -1.0384587138204282 |
| 453.88 | 452.35263519535 | 1.5273648046500057 | 0.34503225112933517 |
| 454.15 | 453.8829146493028 | 0.26708535069718664 | 6.033467545812348e-2 |
| 455.22 | 449.0737291884585 | 6.146270811541513 | 1.388444756419958 |
| 446.44 | 446.2148995417375 | 0.2251004582624887 | 5.085027336501233e-2 |
| 449.6 | 453.8663667211949 | -4.266366721194856 | -0.9637737556054642 |
| 457.89 | 451.0124137711604 | 6.8775862288395615 | 1.553649168586412 |
| 446.02 | 448.5883814092364 | -2.5683814092363946 | -0.5801982713557641 |
| 445.4 | 449.5097304398748 | -4.109730439874795 | -0.9283895640961064 |
| 449.74 | 448.5033053489824 | 1.2366946510176149 | 0.2793697603225793 |
| 448.31 | 446.9172206057899 | 1.3927793942100948 | 0.3146293591732849 |
| 458.63 | 453.8324720669487 | 4.797527933051299 | 1.0837632617676214 |
| 449.93 | 453.30606496401464 | -3.376064964014631 | -0.7626542728669009 |
| 452.39 | 449.08008122232087 | 3.3099187776791155 | 0.7477118259115428 |
| 443.29 | 445.36190546480196 | -2.0719054648019437 | -0.4680441794071581 |
| 446.22 | 449.83432112958235 | -3.614321129582322 | -0.8164764251785102 |
| 439.0 | 444.60209183702824 | -5.60209183702824 | -1.2655145330562003 |
| 452.75 | 455.3330390953461 | -2.5830390953461233 | -0.5835094478470579 |
| 445.65 | 450.28095549994475 | -4.6309554999447755 | -1.046134490045332 |
| 445.27 | 449.3942290809264 | -4.124229080926398 | -0.9316648122523561 |
| 451.95 | 448.76188428428145 | 3.1881157157185385 | 0.7201964710108949 |
| 445.02 | 449.49526226001734 | -4.4752622600173595 | -1.0109633319210711 |
| 450.74 | 449.4914112072868 | 1.2485887927132353 | 0.2820566511504923 |
| 443.93 | 449.5171242611325 | -5.587124261132487 | -1.262133352352325 |
| 445.91 | 447.2640843894497 | -1.3540843894496675 | -0.30588814387272023 |
| 445.71 | 451.75573664689705 | -6.045736646897069 | -1.3657340529671387 |
| 452.7 | 449.47769197848703 | 3.2223080215129585 | 0.7279205250179235 |
| 449.38 | 448.9807967933042 | 0.3992032066958018 | 9.01801459906393e-2 |
| 445.09 | 452.83851864090616 | -7.748518640906184 | -1.7503931093934726 |
| 450.22 | 448.99070865761826 | 1.2292913423817708 | 0.27769735027577597 |
| 441.41 | 448.9314689751323 | -7.521468975132279 | -1.6991025093602443 |
| 462.19 | 458.19122302999057 | 3.9987769700094304 | 0.9033251358981357 |
| 446.17 | 449.6590005617065 | -3.4890005617064617 | -0.7881664644439343 |
| 444.19 | 446.545237358833 | -2.3552373588329942 | -0.532048954767078 |
| 453.15 | 456.3129527756159 | -3.1629527756159064 | -0.7145121539163726 |
| 451.49 | 449.9268730104538 | 1.5631269895462196 | 0.35311094138229787 |
| 453.38 | 448.0350183689646 | 5.3449816310354095 | 1.2074332463249917 |
| 452.1 | 453.06361431502756 | -0.9636143150275416 | -0.21768081556036123 |
| 446.33 | 449.51211271813366 | -3.1821127181336806 | -0.718840391727202 |
| 452.46 | 450.672947544889 | 1.7870524551109952 | 0.40369578348012486 |
| 444.87 | 447.9552056621943 | -3.085205662194312 | -0.6969490534174008 |
| 444.04 | 448.2666586698033 | -4.226658669803271 | -0.9548036927115915 |
| 449.12 | 448.66968221055265 | 0.4503177894473538 | 0.10172694836464449 |
| 443.15 | 445.7146703190735 | -2.564670319073514 | -0.5793599347716393 |
| 446.84 | 452.8021698612517 | -5.962169861251709 | -1.3468562864485707 |
| 443.93 | 446.7443660008083 | -2.8143660008083202 | -0.6357662778430159 |
| 455.18 | 448.49796091182566 | 6.682039088174349 | 1.5094750001492365 |
| 451.88 | 449.90863388073797 | 1.9713661192620293 | 0.44533230558821424 |
| 458.47 | 453.53620348431275 | 4.933796515687277 | 1.1145463829197875 |
| 449.26 | 451.60241101975805 | -2.3424110197580603 | -0.5291514802205262 |
| 444.16 | 448.28015134415546 | -4.120151344155431 | -0.9307436500694146 |
| 441.05 | 445.940139097113 | -4.890139097112979 | -1.1046841565784926 |
| 447.88 | 452.5181226448485 | -4.638122644848522 | -1.047753550621287 |
| 444.91 | 449.080854354919 | -4.1708543549189585 | -0.9421974781853397 |
| 439.01 | 443.764677908941 | -4.754677908941005 | -1.0740834261221295 |
| 453.17 | 449.2796285666682 | 3.890371433331836 | 0.8788362867110385 |
-- Now we can display the RMSE as a Histogram. Clearly this shows that the RMSE is centered around 0 with the vast majority of the error within 2 RMSEs.
SELECT Within_RSME from Power_Plant_RMSE_Evaluation
| Within_RSME |
|---|
| -0.6545286058914855 |
| -1.4178948518800556 |
| 0.17524239493619823 |
| 0.44163480044197995 |
| 0.26132139752130357 |
| 0.28066570579802425 |
| 1.3625651590092023 |
| 0.9078489886839033 |
| -0.3442308494884213 |
| -0.11630232674577932 |
| 1.7506729821805804 |
| -8.284953745386567e-2 |
| -1.692818871916148 |
| 2.608373132048146e-2 |
| 0.8783285919200484 |
| 1.8262340682999108 |
| 0.9515196517846441 |
| 0.9951418870719423 |
| -0.12833292050229034 |
| 1.8547757064958097 |
| -6.520499829010114e-2 |
| 1.7540228045303743 |
| 1.712912449331917 |
| -0.567063215206105 |
| 1.758470293691663 |
| 0.36292285373571487 |
| 1.3722994239368362 |
| -0.4518791902667791 |
| -0.711637096481805 |
| 0.7477551488923485 |
| -9.075206274161249e-2 |
| 0.33498359634397323 |
| -2.717087121436152 |
| 1.6347795140090344 |
| 0.5413939250993844 |
| 1.8721087453250556 |
| 1.2897763086344445 |
| -0.6298581141929721 |
| -0.3869554869711247 |
| 1.5590952476122018 |
| -5.164688385271067e-2 |
| 1.021860350507925 |
| 4.2073297187840204e-2 |
| 3.6816563372465104e-2 |
| 2.4273181887690556 |
| 0.2760063132279849 |
| -0.2711988922825122 |
| -0.49565448805243767 |
| 1.6046669568831393 |
| -0.5946690382578993 |
| 1.1099602752189677 |
| 0.42249584921569994 |
| 0.3776264153858503 |
| 1.1253464244922151 |
| 2.313691606156222 |
| 0.15702246269194528 |
| 1.1180526463224945 |
| 1.0326707046913566 |
| -0.5009264197587914 |
| 2.1055253303633577 |
| 0.883079580425271 |
| 0.8562239455498787 |
| 1.0581479782654901 |
| -0.5881175213603873 |
| -1.0469362575856187 |
| 0.9845051707078616 |
| -0.32033221960491765 |
| -6.11464435454739e-2 |
| 1.7966491781271794 |
| 1.449961394951398 |
| 0.1429791665816065 |
| 0.17796926331974147 |
| 0.590178010121793 |
| -0.4427473424095181 |
| -1.4586722394340677 |
| -0.14061563348410486 |
| -0.4778797517789611 |
| 0.16287170617772706 |
| 1.6205038004183012 |
| 0.6655668633335223 |
| 0.9552928474721311 |
| -0.9255614318409298 |
| -0.20841065925050425 |
| -0.5044470235035043 |
| -0.9064055675522111 |
| -0.5331117277702286 |
| 1.0604764411029242 |
| 0.6163555912803304 |
| 1.502475041125319 |
| 0.11025852973791857 |
| 1.027322682875218e-2 |
| 1.2068500286116204 |
| 0.7807519242835766 |
| 0.5378958494817917 |
| 1.4921557049542535 |
| 0.8423417649127792 |
| -0.8074228949228237 |
| -1.273287251952606 |
| -0.8366248339128552 |
| 0.5703862219885535 |
| 0.9915076732202934 |
| 1.392578067734949 |
| -1.1161712376708273 |
| -0.5177873225895746 |
| 1.2543530104022107 |
| 0.8332547944829863 |
| -0.15024202389261102 |
| -0.7461833621634392 |
| -2.6333071255094267 |
| 1.7090186657480726 |
| 1.17171695524459 |
| 0.16538389150158914 |
| -0.6043651708916123 |
| 1.5140936302699408 |
| -0.40248969630754267 |
| -0.5274788489010923 |
| 1.673819828943716 |
| 0.3998536629456538 |
| 1.5247431144210337 |
| 0.681669465709775 |
| -0.5067930534728822 |
| 1.3329317905059794 |
| 0.2815190232236669 |
| 0.9594271583094607 |
| 0.7195804749163808 |
| 1.563628994525865 |
| -0.5164095221527084 |
| 0.5408652916763936 |
| 1.2169907361146226 |
| 1.3691416206809235 |
| 0.9990582667496749 |
| 0.2866573009496584 |
| -0.1322508701375881 |
| -0.11350226422907182 |
| 1.5478953375265612 |
| 0.6261411303745675 |
| -0.3570008639436786 |
| 0.11097880108736301 |
| -1.997789295120947e-2 |
| 0.8015184563019332 |
| 1.0694995653480546 |
| -0.44658623651141477 |
| 1.5261372030315674 |
| 9.637974736911713e-2 |
| -0.8289854400119429 |
| 1.6226588690300885 |
| 1.2686705940133918 |
| -0.17369127241360752 |
| 1.465503060651426 |
| -0.8493901094223798 |
| -3.19863982083975e-3 |
| 0.775250166241454 |
| -1.476480677907779 |
| 1.6164395333609067 |
| -0.7292074491181413 |
| -2.0719934236347326 |
| 6.689462100697262e-2 |
| 0.41987991622217147 |
| 1.5698655082870474 |
| 0.9108140262190783 |
| 1.4283829990671366 |
| -0.7468033961054397 |
| 1.8565224554443056e-2 |
| -0.631158789082455 |
| -2.423658794064658e-2 |
| 0.1268789228414677 |
| 1.2467256943415417 |
| 0.9532625827884375 |
| 1.858427070746631 |
| 3.080438049074982 |
| -0.7935508099728905 |
| -0.33001241533337206 |
| 1.5352270267793358 |
| -0.8014312542128782 |
| 1.0796607821279804 |
| 0.8682110816429235 |
| 0.8502714264690703 |
| 0.271857163937664 |
| 1.4407225588170043 |
| -7.658599891256687e-2 |
| 0.4048751439362759 |
| -1.6508290646045036 |
| 0.9392995195813987 |
| 0.3512225476325008 |
| 1.3510289270963245e-2 |
| 1.214207255223955 |
| 1.357197397311141 |
| 0.7924624787403274 |
| 0.9747529486084029 |
| -0.2189330666129607 |
| 1.1957383378087658 |
| -0.33808517274788225 |
| 0.12841983400410018 |
| 2.6901995797195863e-2 |
| 0.6811317532938489 |
| -0.14977990817360218 |
| 0.3021002532054435 |
| 6.309664277182285e-2 |
| -1.4280877316330394 |
| -0.31036099175789383 |
| 0.23847593115995214 |
| -1.2544261801023373 |
| 1.487015909020143 |
| 1.4770822031817277e-2 |
| 1.5464079765310332 |
| -0.9661142653632034 |
| 0.12365480382094843 |
| 0.2213694799459989 |
| -0.371717945334288 |
| -7.16964482466269e-2 |
| -1.3370460279083884 |
| -0.4791979479015218 |
| 0.7258131397464832 |
| -0.9603785228015735 |
| 1.761740374853143 |
| 0.756705284425051 |
| -7.97943379405968e-2 |
| -0.12401745329336308 |
| 0.6066524918978817 |
| -1.827662052785967 |
| -1.591900543337891 |
| -2.2076662292962226 |
| -0.6113138148859982 |
| -0.6998705392764937 |
| -0.950596077665842 |
| 0.8530061355541199 |
| -0.3389719928622738 |
| 1.166670943264595 |
| -0.793497343451686 |
| -0.17702361157448407 |
| -1.2193083364383357 |
| -2.0572343953484604 |
| -0.3306196669058454 |
| 0.30773883474602903 |
| -0.23209777066088685 |
| 1.4407294606630792 |
| -0.25326355704561365 |
| -4.4359271514481574e-2 |
| 0.6878454023126037 |
| -1.1071350441930146 |
| 1.1936865467290878 |
| -0.3007124612846165 |
| 1.4096765924648265 |
| 0.6164656679054759 |
| 0.9857137966698644 |
| 1.0741485680349747 |
| -0.5956816180527323 |
| 0.42955444320288233 |
| 0.3977787059312943 |
| -0.21498609418317555 |
| 0.9605949185920998 |
| -0.9629884170069595 |
| 1.0232242351169787 |
| -0.5846231192914554 |
| 1.3665650089641064 |
| 1.746749569478921 |
| 0.20876984487810568 |
| 1.2521345827220822 |
| 0.5034033523179731 |
| 0.2428222722587704 |
| -0.8164837978146291 |
| 0.8816626861116392 |
| 0.3639355749527803 |
| 0.529553624374745 |
| -0.5182949859646964 |
| 1.1483434385624023 |
| 1.7878124840322416 |
| 1.1154300586630213 |
| 1.7220135063654567 |
| -0.5948541705999838 |
| 2.6840159315323495 |
| 1.4048914641301884 |
| 1.048062749037262 |
| 1.0587197080412232 |
| 2.305613395286325 |
| -0.36112584123348884 |
| -0.9292014386335458 |
| 1.3994718175237673 |
| -2.07160847091363 |
| 1.5499716375563712 |
| -0.49879093666907914 |
| -0.7387667159730148 |
| 1.215743751168344 |
| 0.41488138983573053 |
| 0.8109415219227073 |
| 0.17199705539157537 |
| 0.37238038749473606 |
| 1.0232860206712358 |
| 7.648152721354506e-2 |
| -1.462397837959548 |
| 1.1080799981170868 |
| -0.8241779060158768 |
| 0.6646125250731286 |
| -0.5357411851906123 |
| -1.1013185729957693 |
| 1.0228434545117104 |
| -2.2138675491121655 |
| 1.1090799040003696 |
| 0.528855769995572 |
| 0.5510063260220736 |
| -0.6178137048585499 |
| -1.5423062021582727 |
| 0.14223087889069697 |
| 0.6842724817647582 |
| 0.8947164246665408 |
| 0.2929208050392189 |
| 1.0509433168534397 |
| -0.7432089035850772 |
| -0.4404227630306511 |
| -1.749430050046347 |
| -1.8086930425174812 |
| -1.0601131663478296 |
| 1.1367001553490168 |
| 0.8092747458990647 |
| -0.6912565984961676 |
| 0.5876026195201212 |
| 0.588118260475777 |
| -1.5843005948416944 |
| 1.2258227543850637 |
| -2.221379961092406 |
| 0.634487138275479 |
| 0.5838507185000019 |
| -0.7046913242897344 |
| -0.37217013370970675 |
| -0.40920885267913865 |
| 0.6878875094589886 |
| -0.6603570138111878 |
| 0.47983904985387016 |
| 1.1108376145467862 |
| -1.2967007380916762 |
| 1.597843788860663 |
| -1.2003451799842708 |
| 0.7823286855941306 |
| 0.4040957861116898 |
| -0.8115843408453783 |
| -0.5164400209043697 |
| 0.6648789506575099 |
| 1.302692092451488 |
| 0.6081791894050589 |
| 0.9892527182470402 |
| 0.8010661900831437 |
| 1.3632196657801583 |
| -0.95536478346845 |
| 1.0773296627734226 |
| -1.2863290875171947 |
| -0.45717742235729764 |
| -1.3782059158917384 |
| 1.3292415395636934 |
| 0.898840426845182 |
| -0.49778501447760914 |
| -2.8013349681766146 |
| -0.4810779148314091 |
| -0.30893260677359485 |
| 1.4144446986124883 |
| 0.8455721466933415 |
| 1.149027173540852 |
| 1.398381410079887 |
| -2.07482799048434 |
| 0.802797467810633 |
| -1.5148720297345803 |
| 0.4457711691982717 |
| 0.8083230187436264 |
| -0.3652639670874125 |
| -1.301009165777192 |
| 0.12351025454739102 |
| 1.1417131567412993 |
| -0.36627110968185583 |
| 0.11986193019333387 |
| 0.9789636158092969 |
| 0.8287590631587972 |
| 0.12784192086705481 |
| -0.2933818764583589 |
| 0.37057045258220545 |
| 0.17867647626623853 |
| 1.4063653898508444 |
| 0.3724177883609634 |
| 0.47087976564737744 |
| -0.29605954815653446 |
| 0.3100202222752913 |
| -0.7571192782768728 |
| -0.159362104405368 |
| 0.4948614777371899 |
| -0.914693478650593 |
| 0.1552739582781331 |
| -0.47397010386004174 |
| 0.9853374925715822 |
| -0.27611557009437565 |
| -0.11876915655659566 |
| -1.9139681704240736 |
| 4.953016947212214e-3 |
| 1.1193515979151634 |
| 0.10914706208866293 |
| 0.2953624624059635 |
| -0.6203480029931175 |
| 1.447373814412471 |
| -1.6476543672481883 |
| -0.688655110626592 |
| 1.119882332695939 |
| -0.489107443803926 |
| 0.36504587152451 |
| -0.6898805978802584 |
| -1.0090851773780105 |
| 1.3164462111456963 |
| -0.9719462634817788 |
| -1.195980679378918 |
| -0.2324693067978492 |
| 1.5483799402320506 |
| -0.7562589912210242 |
| 9.006543612814395e-2 |
| 0.6307060325245487 |
| -0.2658735764273784 |
| 1.3036368729747179 |
| 1.3861689239960837 |
| -0.10256413058630598 |
| 1.7873934636501403 |
| 0.39996285217557137 |
| -0.962793977380421 |
| 1.2120179370324367 |
| -0.14370787512306984 |
| 0.1873907498457296 |
| 8.883711718846676e-2 |
| 1.1389205017346977 |
| -1.5633041348778628e-2 |
| 1.5435878241412961 |
| 0.9211075789572168 |
| 0.17182562184847788 |
| -1.0405053204973118 |
| 0.6134953430761453 |
| 0.924316094817409 |
| 0.893158168449016 |
| 0.7054993963391207 |
| 1.1169128153424053 |
| 1.2716543656809969 |
| 0.2909263677211387 |
| 0.3156983081862408 |
| -0.3181585485837661 |
| -1.79557827837392 |
| -1.9821852874504897 |
| -0.18315266781990724 |
| 0.29419068616581573 |
| 1.321272422372547 |
| -0.5300977560633852 |
| 0.7301877034905364 |
| 0.35702870934871034 |
| -0.12785771898138407 |
| -1.4568475152442806 |
| 1.1799216369387262 |
| -2.6289350896996853 |
| 1.36938729411264 |
| 0.8658222795557158 |
| -1.961929096387823 |
| -1.7869367782036754 |
| -2.3414119790965118 |
| -0.7070000881461707 |
| 1.094870026989326 |
| 1.1674921223009784 |
| 0.4309575170361471 |
| -0.5608691138957983 |
| 0.9204597474090102 |
| 0.45642908946816274 |
| 1.3262603156304236 |
| 0.6694839282628849 |
| -1.4191468918247268 |
| -2.0503769708593955 |
| -1.559496185458982 |
| -0.8294949891974716 |
| -0.7511122251047269 |
| 0.9255039510005931 |
| -0.7348543381165886 |
| 0.14006818598115695 |
| -8.906989903365076e-2 |
| 0.5960428781510217 |
| -1.02587326449146 |
| -0.19021607612190924 |
| 0.4462389970391413 |
| -0.4412430070274576 |
| -0.5519946201974366 |
| -0.5978564569550795 |
| 1.3285941918109334 |
| 0.9398258266993826 |
| 0.18054354406665346 |
| 0.24526741726554308 |
| 0.6419203730291116 |
| -0.9563898027913122 |
| -0.4574416919740238 |
| -0.49100134055869604 |
| 0.8350348745649541 |
| 1.1230180632103244 |
| -0.2831261948065207 |
| -0.34737057487344897 |
| -4.4197222396898606e-2 |
| 0.4029451403943494 |
| -0.8387790498308709 |
| -0.76724104350462 |
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| -0.5049830367049549 |
| -0.4450784100952837 |
| -1.0964525416975441 |
| -0.24679765115367583 |
| 0.6373400044210754 |
| 0.47325677731185334 |
| -2.895796152379444 |
| 1.0608917942097844 |
| -0.5538446783382351 |
| -1.0739516871848376 |
| 0.6590103675152038 |
| 0.19886190778235394 |
| -0.7684000719143101 |
| -0.9540651350015262 |
| 0.8326866968950778 |
| 1.049265553847453 |
| 1.1564460495859417 |
| -0.9105747398006214 |
| -1.298779737770393 |
| 0.7486049675853867 |
| 1.0125992544722646 |
| -0.552486882193573 |
| 4.083638455563342e-2 |
| -7.43032114617912e-2 |
| 0.38180618893011364 |
| -2.5821601917725587 |
| -3.9183934552884936e-2 |
| -0.7161579504665817 |
| -1.1179393167374423 |
| 0.24473358069515785 |
| 0.37566200116631454 |
| -0.8469696321106422 |
| 0.9536067209045733 |
| 1.040389080973342e-3 |
| -1.9463087774264596 |
| -0.5212577234014426 |
| 2.6198974509955283e-2 |
| -0.22312751932133126 |
| -0.3749580186284632 |
| 0.8158971199557807 |
| 0.5526973095848743 |
| -1.0007418364636667 |
| -0.7893531119518733 |
| 0.948603975708334 |
| -0.5535013570539268 |
| 0.615437313273332 |
| 0.390353931433719 |
| 2.1370626071593804e-2 |
| -4.58603577983067e-2 |
| -1.8585598276086635 |
| -1.7168393323476852 |
| -1.1398906775971094 |
| -0.4823380615634837 |
| 0.96139463327236 |
| -0.28409897841626214 |
| -1.1427184843899973 |
| 0.47191339234564744 |
| -0.7999147952790192 |
| -0.6809096342868951 |
| -0.5802973198353383 |
| -0.15724483814107668 |
| -3.095640160553223e-2 |
| -0.935248060922378 |
| -0.2680652196876797 |
| -1.3834783433962559 |
| 0.24424086946940488 |
| 2.8145362608873965e-2 |
| 2.0243901188534075e-2 |
| 0.8656525590853151 |
| -0.43908603802454604 |
| -0.3372983215369459 |
| -1.527152991853827 |
| -0.8950322545891167 |
| 0.49340212657275334 |
| 0.16455403921586076 |
| -0.2922070048026174 |
| 8.049860352378384e-2 |
| 0.5092872109601911 |
| -1.4149898349271792 |
| 0.26001016007049604 |
| 0.848179116862477 |
| 1.541775892767944 |
| -2.7383106694634813 |
| -1.3360086450624162 |
| 0.24805635526298545 |
| -1.8022030869799206 |
| 0.10731148881574702 |
| -0.5272965884493478 |
| -0.522827282380973 |
| -1.6790858913831084 |
| -1.0881066592743878 |
| -0.7551911549160485 |
| 1.0908758941061858 |
| -0.40596025641974887 |
| -1.3175361850815699 |
| -0.33736351432728534 |
| -0.7647965508049966 |
| -0.8966825099514081 |
| 0.13589183843986818 |
| -0.45700135918716 |
| 0.17800963180524587 |
| -0.12241676923652416 |
| 0.928379507484781 |
| -1.4663326638006033 |
| 0.9926875580120624 |
| -0.9519902891303528 |
| -1.2103639004447255 |
| -0.29320537464645474 |
| 1.0589301445025614 |
| 1.9848035286394352 |
| -1.262990865781086 |
| -0.4669510069602996 |
| -0.29852124969696514 |
| 0.4528378506109543 |
| -2.176827775976124 |
| 0.6287618750023547 |
| -0.22259591077043436 |
| 0.8980186936604918 |
| -0.6701335093722809 |
| 5.3540678924927795e-2 |
| -2.0162206655848456 |
| -5.915502468864325 |
| 6.248484005862629e-2 |
| -6.430908490699098e-2 |
| 0.24975508158417795 |
| 0.10082671294548266 |
| 6.192719483296151e-2 |
| -0.8827535896932235 |
| 0.14629114201469284 |
| 0.3647103786297681 |
| 0.13525709550549558 |
| -0.6839012648279135 |
| -0.3282810590165173 |
| -2.5448723679212413 |
| 0.9859079039422615 |
| 0.8870212178332524 |
| -0.9336885155326297 |
| -0.5912519373226456 |
| -1.19738092926328 |
| -4.418517142015048e-2 |
| -1.0477395899387958 |
| 0.8720418466790518 |
| -0.16818902827121646 |
| 0.8106803127226195 |
| 7.651700053300835e-2 |
| 0.6479284215091841 |
| -1.2656945480299158 |
| -0.3928384325809046 |
| -1.6427960713292782 |
| -1.1104398571567456e-2 |
| 0.33300423009965163 |
| -1.4810644003074978 |
| -0.7936240133313774 |
| -0.41607342702266226 |
| -0.8103843372605553 |
| 6.1934850072404936e-2 |
| -1.3972529144877643 |
| -1.1492657301278992 |
| 0.19051481992981023 |
| 1.4771174105289492e-3 |
| -0.7526971048807434 |
| -0.6958979478771827 |
| 0.3857577737865013 |
| -0.26761154566176326 |
| 6.330220478322962e-2 |
| -1.8292445149531718 |
| -0.4450769376655955 |
| 7.679881883917433e-2 |
| -2.4157429316491066 |
| -0.35061571253514523 |
| 0.39710873785339107 |
| -1.1752337481862478 |
| -0.488644466470834 |
| -1.1819154926380382 |
| 0.9611401946105317 |
| -0.88356932939405 |
| -0.3682206690752407 |
| 0.5300804829424589 |
| 0.7517433153011605 |
| -4.100082896844213 |
| -0.18268097898348856 |
| 0.4756183235788339 |
| 0.5490073718413218 |
| 0.11953038666589516 |
| -0.5421149051411095 |
| 2.5064114496629736e-2 |
| 0.230194477556215 |
| -0.5430905347252163 |
| 1.5584512436402695 |
| 0.7308508901811838 |
| -0.4223660350121439 |
| 0.6880017317073447 |
| 0.3163521132445753 |
| 2.2499178761738656e-2 |
| 0.5033732580836815 |
| 1.908085184261808 |
| -4.159840990620793e-2 |
| -0.7448439804538625 |
| -0.681420065430807 |
| 0.3141463932063569 |
| -1.573602447113043 |
| 9.865523425471445e-2 |
| -0.24893221002717708 |
| 0.9263087968044336 |
| -0.43302211618029557 |
| -1.4398587970092795 |
| 0.462885224472874 |
| -0.391055252551079 |
| -1.3270367364908389 |
| 0.5095691560296709 |
| -0.9111937635582255 |
| 0.25748127626527917 |
| 1.0055534371830066 |
| 0.7936454594404486 |
| 0.26066651094965915 |
| 1.3394018178342768 |
| -1.5012794324329175 |
| -1.212821534560818 |
| -0.2995168034930508 |
| 0.14237964077547013 |
| -0.44775226629445647 |
| 0.18686224516459876 |
| -0.2913595677715297 |
| -0.14825636999077735 |
| 1.4255082955353757e-2 |
| -3.85223285385276e-2 |
| 0.34337513637978356 |
| -1.3596032767443251 |
| -0.12697006859821677 |
| -0.9881327478480624 |
| -0.11141412701259497 |
| 0.22977278357028447 |
| 0.5176994104510022 |
| 6.13987745130998e-2 |
| -0.3360717809204362 |
| -0.6528018638498932 |
| 0.5701728960887047 |
| -0.5637272529976811 |
| -1.3206794239848478 |
| 0.1462347705134622 |
| 1.358693285939825 |
| -0.17779587530777846 |
| -1.6381157635658086 |
| -0.602344532375961 |
| -0.3040307722514201 |
| -0.8193791814630933 |
| -1.4755414229551909 |
| -1.1278599078269054 |
| -0.4444726441730127 |
| 0.35879357661949324 |
| 1.1179138541897764e-2 |
| 0.34967140754299175 |
| -6.982165672536109e-2 |
| -1.904553463477631e-2 |
| 3.3509032578186985e-2 |
| 0.43458901734435373 |
| -0.5026269642909126 |
| -1.684639094666372 |
| -0.9597412475447774 |
| 1.4023843420494426 |
| -0.4267339931080307 |
| -9.266528554109059e-2 |
| 0.47488555394598253 |
| 0.18823213582988355 |
| 0.8406032598983453 |
| -0.9910323928287407 |
| -0.9427234831869965 |
| 1.4986945451227311 |
| 0.7610470759162783 |
| 0.21440090198276052 |
| -1.8951086283122098 |
| -0.1195509825335976 |
| 0.10885346370435622 |
| -1.580719694938195 |
| -0.7372165177419857 |
| -0.3453896181957068 |
| -0.1679209953140206 |
| 0.10817078505437988 |
| 0.623898427209341 |
| 0.2059262926120357 |
| -1.4863981874464185 |
| -6.331581426263354e-2 |
| 0.5539275575714465 |
| -0.4114802468703741 |
| -0.2537329263193067 |
| -0.9506562296769948 |
| 1.068374879236074 |
| -0.3398067292862239 |
| -0.32398605358520316 |
| -0.42650686456142684 |
| -2.186502303726239 |
| 1.3938624683483432 |
| -1.0204544914921314 |
| 0.27469550529997006 |
| 0.5362520089571434 |
| -0.7363178909964009 |
| -0.6292901762011418 |
| 0.9369030019327613 |
| 6.551404180844994e-2 |
| 0.1282331586527475 |
| -1.5197729065410778e-2 |
| -1.0950489304100275 |
| -1.3549368516581415 |
| -0.5911666411407626 |
| -0.2532802591719359 |
| -0.9296320860244299 |
| -0.1002787866878538 |
| -1.5821547576776454 |
| -0.9551294929945369 |
| -0.13367191347958032 |
| 0.2685265644141194 |
| 0.6043227304296972 |
| -0.13541855239802086 |
| 0.22096438768429855 |
| 0.6957725134313342 |
| -1.566346070374885 |
| 0.5572636952570301 |
| 0.15486117417577897 |
| 0.3014926601172488 |
| -0.7621478077785558 |
| 0.32734086830965053 |
| -1.6237166505279563 |
| -0.9060064551221478 |
| -0.6544566088606873 |
| -0.21304296358942265 |
| 0.5239325837337663 |
| 0.9223081331700905 |
| -0.9122812185222428 |
| -0.24865928174635601 |
| 0.8634327460307729 |
| -0.7128132140281332 |
| -1.5087079840447288 |
| 1.4460438896255918 |
| 0.22108497623179488 |
| -0.18608980078521345 |
| -0.5136565645795023 |
| -0.2840762845598929 |
| -0.8459874564799138 |
| -0.380059650479177 |
| -1.1646367865259744 |
| 0.5960061104441506 |
| 0.29675974405004724 |
| -2.2198394643539735e-2 |
| 1.0556739178415897 |
| 0.35023171132628417 |
| -1.2014380501041335 |
| 0.5189884975744112 |
| 0.38295900412869216 |
| 0.9431311483218855 |
| 3.170143276445295e-2 |
| 1.3307727287321278 |
| -1.3597476078635353 |
| 1.6925820719064946 |
| 0.10701392309290869 |
| 0.3769110689022767 |
| -0.5225688650866008 |
| 0.12962105610924668 |
| 0.4388507070002017 |
| -1.5635346785212256 |
| 0.7400421406892627 |
| -0.1542292630166233 |
| -4.453077099148361e-2 |
| -0.5182229388235478 |
| -0.48273405745988357 |
| 0.34946624425526 |
| 0.1975687811786549 |
| -0.34106306503697253 |
| -1.0431049581122194 |
| 0.5473914836897852 |
| -0.32163941044764294 |
| -2.1123605476138505 |
| 0.6755552672777567 |
| -0.7768750359376887 |
| 7.361579398740335e-2 |
| -1.1767524912633618 |
| -0.5872458115946685 |
| -0.15834020050780284 |
| 0.682572359347037 |
| 0.25945073138280605 |
| 0.18198122034572886 |
| 0.8813451011277746 |
| 9.289521984221884e-2 |
| -0.8224646596460314 |
| -1.0992548944297087 |
| 0.24767764457942207 |
| -1.6296349827369583 |
| 0.8839992806447705 |
| -0.20826509006315633 |
| -1.1308211710304394 |
| -0.888034323093527 |
| 1.375146556884749 |
| -0.12272814800411408 |
| 0.4708084873186104 |
| -0.4634347195545461 |
| 0.3440522256895429 |
| 0.4367497353673552 |
| -0.2795279825620374 |
| 0.7701118542900588 |
| -4.2075245099282746e-2 |
| -0.10357117246458049 |
| 1.80757939097979e-2 |
| 0.16185831370575537 |
| 0.5535246598913276 |
| -0.4376793572220653 |
| 0.2960230050324334 |
| -0.501790599000596 |
| -0.4316894674828857 |
| 0.24271962137276715 |
| -0.6012591933934375 |
| 0.499528227037766 |
| -0.227724741199252 |
| -1.5266390839325845 |
| -0.5833859951647822 |
| 0.44092803915740614 |
| 1.3593919171619417 |
| 0.637089242037123 |
| 0.712636779143946 |
| -1.1258805595962003 |
| 0.1451010950402414 |
| -0.6625051797404924 |
| -0.8793761681170623 |
| 0.3205158072965067 |
| 0.11039293412506312 |
| -0.935517944613967 |
| -0.35882328154752013 |
| -0.4990646122154142 |
| -0.5813521178437366 |
| -3.959793091748769e-2 |
| -1.016322415917227 |
| 1.069329943682345 |
| -6.899807012373943e-2 |
| -0.9360632713678595 |
| -0.600779764980383 |
| -1.0163490721896018 |
| -0.7464384026808953 |
| 0.21876928556414296 |
| 0.23953823855339262 |
| -1.349843212892932 |
| -0.9621476427127764 |
| 0.599309142680298 |
| -0.7426591743046266 |
| 2.999965310254963e-2 |
| 1.4104064512304106 |
| -0.281655180427759 |
| -0.9878062656356458 |
| -0.4153208114916822 |
| 5.918848182094669e-2 |
| -0.4991855384391755 |
| 0.37554324623667645 |
| -1.0384587138204282 |
| 0.34503225112933517 |
| 6.033467545812348e-2 |
| 1.388444756419958 |
| 5.085027336501233e-2 |
| -0.9637737556054642 |
| 1.553649168586412 |
| -0.5801982713557641 |
| -0.9283895640961064 |
| 0.2793697603225793 |
| 0.3146293591732849 |
| 1.0837632617676214 |
| -0.7626542728669009 |
| 0.7477118259115428 |
| -0.4680441794071581 |
| -0.8164764251785102 |
| -1.2655145330562003 |
| -0.5835094478470579 |
| -1.046134490045332 |
| -0.9316648122523561 |
| 0.7201964710108949 |
| -1.0109633319210711 |
| 0.2820566511504923 |
| -1.262133352352325 |
| -0.30588814387272023 |
| -1.3657340529671387 |
| 0.7279205250179235 |
| 9.01801459906393e-2 |
| -1.7503931093934726 |
| 0.27769735027577597 |
| -1.6991025093602443 |
| 0.9033251358981357 |
| -0.7881664644439343 |
| -0.532048954767078 |
| -0.7145121539163726 |
| 0.35311094138229787 |
| 1.2074332463249917 |
| -0.21768081556036123 |
| -0.718840391727202 |
| 0.40369578348012486 |
| -0.6969490534174008 |
| -0.9548036927115915 |
| 0.10172694836464449 |
| -0.5793599347716393 |
| -1.3468562864485707 |
| -0.6357662778430159 |
| 1.5094750001492365 |
| 0.44533230558821424 |
| 1.1145463829197875 |
| -0.5291514802205262 |
| -0.9307436500694146 |
| -1.1046841565784926 |
| -1.047753550621287 |
| -0.9421974781853397 |
| -1.0740834261221295 |
| 0.8788362867110385 |
We can see this definitively if we count the number of predictions within + or - 1.0 and + or - 2.0 and display this as a pie chart:
SELECT case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1 when Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end RSME_Multiple, COUNT(*) count from Power_Plant_RMSE_Evaluation
group by case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1 when Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end
| RSME_Multiple | count |
|---|---|
| 1.0 | 1267.0 |
| 3.0 | 77.0 |
| 2.0 | 512.0 |
So we have about 70% of our training data within 1 RMSE and about 97% (70% + 27%) within 2 RMSE. So the model is pretty decent. Let's see if we can tune the model to improve it further.
NOTE: these numbers will vary across runs due to the seed in random sampling of training and test set, number of iterations, and other stopping rules in optimization, for example.
Step 7: Tuning and Evaluation
Now that we have a model with all of the data let's try to make a better model by tuning over several parameters.
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
First let's use a cross validator to split the data into training and validation subsets. See http://spark.apache.org/docs/latest/ml-tuning.html.
//Let's set up our evaluator class to judge the model based on the best root mean squared error
val regEval = new RegressionEvaluator()
regEval.setLabelCol("PE")
.setPredictionCol("Predicted_PE")
.setMetricName("rmse")
regEval: org.apache.spark.ml.evaluation.RegressionEvaluator = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
res101: regEval.type = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
We now treat the lrPipeline as an Estimator, wrapping it in a CrossValidator instance.
This will allow us to jointly choose parameters for all Pipeline stages.
A CrossValidator requires an Estimator, an Evaluator (which we set next).
//Let's create our crossvalidator with 3 fold cross validation
val crossval = new CrossValidator()
crossval.setEstimator(lrPipeline)
crossval.setNumFolds(3)
crossval.setEvaluator(regEval)
crossval: org.apache.spark.ml.tuning.CrossValidator = cv_bc136566b6a6
res102: crossval.type = cv_bc136566b6a6
A CrossValidator also requires a set of EstimatorParamMaps which we set next.
For this we need a regularization parameter (more generally a hyper-parameter that is model-specific).
Now, let's tune over our regularization parameter from 0.01 to 0.10.
val regParam = ((1 to 10) toArray).map(x => (x /100.0))
warning: there was one feature warning; for details, enable `:setting -feature' or `:replay -feature'
regParam: Array[Double] = Array(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1)
Check out the scala docs for syntactic details on org.apache.spark.ml.tuning.ParamGridBuilder.
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, regParam)
.build()
crossval.setEstimatorParamMaps(paramGrid)
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
linReg_3b8dffe4015f-regParam: 0.01
}, {
linReg_3b8dffe4015f-regParam: 0.02
}, {
linReg_3b8dffe4015f-regParam: 0.03
}, {
linReg_3b8dffe4015f-regParam: 0.04
}, {
linReg_3b8dffe4015f-regParam: 0.05
}, {
linReg_3b8dffe4015f-regParam: 0.06
}, {
linReg_3b8dffe4015f-regParam: 0.07
}, {
linReg_3b8dffe4015f-regParam: 0.08
}, {
linReg_3b8dffe4015f-regParam: 0.09
}, {
linReg_3b8dffe4015f-regParam: 0.1
})
res103: crossval.type = cv_bc136566b6a6
//Now let's create our model
val cvModel = crossval.fit(trainingSet)
cvModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_07136143d91d, numFolds=3
In addition to CrossValidator Spark also offers TrainValidationSplit for hyper-parameter tuning. TrainValidationSplit only evaluates each combination of parameters once as opposed to k times in case of CrossValidator. It is therefore less expensive, but will not produce as reliable results when the training dataset is not sufficiently large.
Now that we have tuned let's see what we got for tuning parameters and what our RMSE was versus our intial model
val predictionsAndLabels = cvModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@671c9416
rmse: Double = 4.4286032895695895
explainedVariance: Double = 271.5750243564875
r2: Double = 0.9313732865178349
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 4.4286032895695895
Explained Variance: 271.5750243564875
R2: 0.9313732865178349
Let us explore other models to see if we can predict the power output better
There are several families of models in Spark's scalable machine learning library:
So our initial untuned and tuned linear regression models are statistically identical.
Given that the only linearly correlated variable is Temperature, it makes sense try another machine learning method such a Decision Tree to handle non-linear data and see if we can improve our model
A Decision Tree creates a model based on splitting variables using a tree structure. We will first start with a single decision tree model.
Reference Decision Trees: https://en.wikipedia.org/wiki/Decisiontreelearning
//Let's build a decision tree pipeline
import org.apache.spark.ml.regression.DecisionTreeRegressor
// we are using a Decision Tree Regressor as opposed to a classifier we used for the hand-written digit classification problem
val dt = new DecisionTreeRegressor()
dt.setLabelCol("PE")
dt.setPredictionCol("Predicted_PE")
dt.setFeaturesCol("features")
dt.setMaxBins(100)
val dtPipeline = new Pipeline()
dtPipeline.setStages(Array(vectorizer, dt))
import org.apache.spark.ml.regression.DecisionTreeRegressor
dt: org.apache.spark.ml.regression.DecisionTreeRegressor = dtr_92691f930484
dtPipeline: org.apache.spark.ml.Pipeline = pipeline_ddfeb1cfff04
res106: dtPipeline.type = pipeline_ddfeb1cfff04
//Let's just resuse our CrossValidator
crossval.setEstimator(dtPipeline)
res107: crossval.type = cv_bc136566b6a6
val paramGrid = new ParamGridBuilder()
.addGrid(dt.maxDepth, Array(2, 3))
.build()
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
dtr_92691f930484-maxDepth: 2
}, {
dtr_92691f930484-maxDepth: 3
})
crossval.setEstimatorParamMaps(paramGrid)
res108: crossval.type = cv_bc136566b6a6
val dtModel = crossval.fit(trainingSet) // fit decitionTree with cv
dtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_ddfeb1cfff04, numFolds=3
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel].toDebugString
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
res109: String =
"DecisionTreeRegressionModel: uid=dtr_92691f930484, depth=3, numNodes=15, numFeatures=4
If (feature 0 <= 18.595)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 8.575)
Predict: 483.994943457189
Else (feature 0 > 8.575)
Predict: 476.04374262101527
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 15.475000000000001)
Predict: 467.5564649956784
Else (feature 0 > 15.475000000000001)
Predict: 459.5010376134889
Else (feature 0 > 18.595)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 22.055)
Predict: 452.01076923076914
Else (feature 0 > 22.055)
Predict: 443.46937926330156
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: 440.73731707317074
Else (feature 0 > 25.325)
Predict: 433.86131215469624
"
The line above will pull the Decision Tree model from the Pipeline and display it as an if-then-else string.
Next let's visualize it as a decision tree for regression.
display(dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel])
| treeNode |
|---|
| {"index":7,"featureType":"continuous","prediction":null,"threshold":18.595,"categories":null,"feature":0,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":11.885000000000002,"categories":null,"feature":0,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":8.575,"categories":null,"feature":0,"overflow":false} |
| {"index":0,"featureType":null,"prediction":483.994943457189,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":476.04374262101527,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":15.475000000000001,"categories":null,"feature":0,"overflow":false} |
| {"index":4,"featureType":null,"prediction":467.5564649956784,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":459.5010376134889,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":66.21000000000001,"categories":null,"feature":1,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":22.055,"categories":null,"feature":0,"overflow":false} |
| {"index":8,"featureType":null,"prediction":452.01076923076914,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":443.46937926330156,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":25.325,"categories":null,"feature":0,"overflow":false} |
| {"index":12,"featureType":null,"prediction":440.73731707317074,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":433.86131215469624,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Now let's see how our DecisionTree model compares to our LinearRegression model
val predictionsAndLabels = dtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 5.111944542729953
Explained Variance: 261.4355220034205
R2: 0.908560906504635
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@684c4c50
rmse: Double = 5.111944542729953
explainedVariance: Double = 261.4355220034205
r2: Double = 0.908560906504635
So our DecisionTree was slightly worse than our LinearRegression model (LR: 4.6 vs DT: 5.2). Maybe we can try an Ensemble method such as Gradient-Boosted Decision Trees to see if we can strengthen our model by using an ensemble of weaker trees with weighting to reduce the error in our model.
*Note since this is a complex model, the cell below can take about *16 minutes* or so to run on a small cluster with a couple nodes with about 6GB RAM, go out and grab a coffee and come back :-).*
This GBTRegressor code will be way faster on a larger cluster of course.
A visual explanation of gradient boosted trees:
Let's see what a boosting algorithm, a type of ensemble method, is all about in more detail.
import org.apache.spark.ml.regression.GBTRegressor
val gbt = new GBTRegressor()
gbt.setLabelCol("PE")
gbt.setPredictionCol("Predicted_PE")
gbt.setFeaturesCol("features")
gbt.setSeed(100088121L)
gbt.setMaxBins(100)
gbt.setMaxIter(120)
val gbtPipeline = new Pipeline()
gbtPipeline.setStages(Array(vectorizer, gbt))
//Let's just resuse our CrossValidator
crossval.setEstimator(gbtPipeline)
val paramGrid = new ParamGridBuilder()
.addGrid(gbt.maxDepth, Array(2, 3))
.build()
crossval.setEstimatorParamMaps(paramGrid)
//gbt.explainParams
val gbtModel = crossval.fit(trainingSet)
import org.apache.spark.ml.regression.GBTRegressor
gbt: org.apache.spark.ml.regression.GBTRegressor = gbtr_0a275b363831
gbtPipeline: org.apache.spark.ml.Pipeline = pipeline_8b2722444cff
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
gbtr_0a275b363831-maxDepth: 2
}, {
gbtr_0a275b363831-maxDepth: 3
})
gbtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
import org.apache.spark.ml.regression.GBTRegressionModel
val predictionsAndLabels = gbtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 3.7034735091301307
Explained Variance: 272.208177448154
R2: 0.9520069744321176
import org.apache.spark.ml.regression.GBTRegressionModel
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@79d7946a
rmse: Double = 3.7034735091301307
explainedVariance: Double = 272.208177448154
r2: Double = 0.9520069744321176
We can use the toDebugString method to dump out what our trees and weighting look like:
gbtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[GBTRegressionModel].toDebugString
res116: String =
"GBTRegressionModel: uid=gbtr_0a275b363831, numTrees=120, numFeatures=4
Tree 0 (weight 1.0):
If (feature 0 <= 18.595)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 8.575)
Predict: 483.994943457189
Else (feature 0 > 8.575)
Predict: 476.04374262101527
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 15.475000000000001)
Predict: 467.5564649956784
Else (feature 0 > 15.475000000000001)
Predict: 459.5010376134889
Else (feature 0 > 18.595)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 22.055)
Predict: 452.01076923076914
Else (feature 0 > 22.055)
Predict: 443.46937926330156
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: 440.73731707317074
Else (feature 0 > 25.325)
Predict: 433.86131215469624
Tree 1 (weight 0.1):
If (feature 3 <= 82.55000000000001)
If (feature 0 <= 26.505000000000003)
If (feature 3 <= 64.11500000000001)
Predict: 6.095232469256877
Else (feature 3 > 64.11500000000001)
Predict: 1.4337156041793564
Else (feature 0 > 26.505000000000003)
If (feature 1 <= 65.55000000000001)
Predict: -7.6527692217902885
Else (feature 1 > 65.55000000000001)
Predict: -0.5844786594493007
Else (feature 3 > 82.55000000000001)
If (feature 1 <= 45.754999999999995)
If (feature 3 <= 93.075)
Predict: 0.6108402960070604
Else (feature 3 > 93.075)
Predict: -3.913147108789527
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 18.595)
Predict: -9.443118256805649
Else (feature 0 > 18.595)
Predict: -3.9650066253122347
Tree 2 (weight 0.1):
If (feature 1 <= 45.754999999999995)
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 14.285)
Predict: 1.1090321642566017
Else (feature 0 > 14.285)
Predict: -3.898383406004269
Else (feature 0 > 15.475000000000001)
If (feature 0 <= 18.595)
Predict: 4.874047316205584
Else (feature 0 > 18.595)
Predict: 10.653531335032094
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 18.595)
If (feature 1 <= 58.19)
Predict: -5.0005796708960615
Else (feature 1 > 58.19)
Predict: -13.749053154552547
Else (feature 0 > 18.595)
If (feature 2 <= 1009.295)
Predict: -3.1410617295379555
Else (feature 2 > 1009.295)
Predict: 0.8001319329379373
Tree 3 (weight 0.1):
If (feature 3 <= 85.52000000000001)
If (feature 1 <= 55.825)
If (feature 0 <= 26.505000000000003)
Predict: 2.695149105254423
Else (feature 0 > 26.505000000000003)
Predict: -6.7564705067621675
Else (feature 1 > 55.825)
If (feature 1 <= 66.21000000000001)
Predict: -2.282355645191304
Else (feature 1 > 66.21000000000001)
Predict: 0.5508297910243797
Else (feature 3 > 85.52000000000001)
If (feature 1 <= 40.629999999999995)
If (feature 2 <= 1022.8399999999999)
Predict: 2.236331037122985
Else (feature 2 > 1022.8399999999999)
Predict: -7.220151471077029
Else (feature 1 > 40.629999999999995)
If (feature 3 <= 89.595)
Predict: -2.175085332960937
Else (feature 3 > 89.595)
Predict: -4.54715329410012
Tree 4 (weight 0.1):
If (feature 2 <= 1010.335)
If (feature 1 <= 43.005)
If (feature 0 <= 15.475000000000001)
Predict: -0.004818717215773318
Else (feature 0 > 15.475000000000001)
Predict: 5.355639866401019
Else (feature 1 > 43.005)
If (feature 1 <= 65.15)
Predict: -5.132340755716604
Else (feature 1 > 65.15)
Predict: -0.7228608840382099
Else (feature 2 > 1010.335)
If (feature 3 <= 74.525)
If (feature 0 <= 29.455)
Predict: 3.0313483965828176
Else (feature 0 > 29.455)
Predict: -2.560863167947768
Else (feature 3 > 74.525)
If (feature 1 <= 40.629999999999995)
Predict: 2.228142113797423
Else (feature 1 > 40.629999999999995)
Predict: -1.6052323952407273
Tree 5 (weight 0.1):
If (feature 3 <= 81.045)
If (feature 0 <= 27.585)
If (feature 3 <= 46.92)
Predict: 8.005444802948366
Else (feature 3 > 46.92)
Predict: 1.301219291279602
Else (feature 0 > 27.585)
If (feature 1 <= 65.55000000000001)
Predict: -6.568175152421089
Else (feature 1 > 65.55000000000001)
Predict: -0.8331220915230161
Else (feature 3 > 81.045)
If (feature 1 <= 41.519999999999996)
If (feature 2 <= 1019.835)
Predict: 1.7054425589091675
Else (feature 2 > 1019.835)
Predict: -2.6266728146418195
Else (feature 1 > 41.519999999999996)
If (feature 1 <= 41.805)
Predict: -11.130585327952137
Else (feature 1 > 41.805)
Predict: -2.111742422947989
Tree 6 (weight 0.1):
If (feature 2 <= 1009.625)
If (feature 1 <= 43.005)
If (feature 0 <= 15.475000000000001)
Predict: -0.4497329015715072
Else (feature 0 > 15.475000000000001)
Predict: 3.661440765591046
Else (feature 1 > 43.005)
If (feature 1 <= 66.85499999999999)
Predict: -3.861085082339984
Else (feature 1 > 66.85499999999999)
Predict: -0.7492674320362704
Else (feature 2 > 1009.625)
If (feature 3 <= 69.32499999999999)
If (feature 0 <= 29.455)
Predict: 2.621042446270476
Else (feature 0 > 29.455)
Predict: -1.4554021183385886
Else (feature 3 > 69.32499999999999)
If (feature 1 <= 58.19)
Predict: 0.42605173456002876
Else (feature 1 > 58.19)
Predict: -1.9554878629891888
Tree 7 (weight 0.1):
If (feature 3 <= 86.625)
If (feature 1 <= 52.795)
If (feature 0 <= 18.595)
Predict: 0.4988124937300424
Else (feature 0 > 18.595)
Predict: 4.447321094438702
Else (feature 1 > 52.795)
If (feature 1 <= 66.21000000000001)
Predict: -1.721618872277871
Else (feature 1 > 66.21000000000001)
Predict: 0.6365209437219329
Else (feature 3 > 86.625)
If (feature 0 <= 6.895)
If (feature 3 <= 87.32499999999999)
Predict: -10.224737687790185
Else (feature 3 > 87.32499999999999)
Predict: 3.712660431036073
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
Predict: -7.105091794629204
Else (feature 0 > 8.575)
Predict: -1.5060749360589718
Tree 8 (weight 0.1):
If (feature 2 <= 1008.745)
If (feature 1 <= 41.665)
If (feature 0 <= 15.475000000000001)
Predict: 0.11492519898093705
Else (feature 0 > 15.475000000000001)
Predict: 3.532699993937119
Else (feature 1 > 41.665)
If (feature 1 <= 66.85499999999999)
Predict: -3.366654434213031
Else (feature 1 > 66.85499999999999)
Predict: -0.9165259817521934
Else (feature 2 > 1008.745)
If (feature 3 <= 82.955)
If (feature 1 <= 51.905)
Predict: 1.841638760642105
Else (feature 1 > 51.905)
Predict: 0.33265178659776373
Else (feature 3 > 82.955)
If (feature 3 <= 95.68)
Predict: -0.4850135884105616
Else (feature 3 > 95.68)
Predict: -3.812639024859598
Tree 9 (weight 0.1):
If (feature 0 <= 29.455)
If (feature 3 <= 61.89)
If (feature 1 <= 71.18)
Predict: 1.9301726185107941
Else (feature 1 > 71.18)
Predict: 8.938327477606107
Else (feature 3 > 61.89)
If (feature 0 <= 5.8149999999999995)
Predict: 4.428862111265314
Else (feature 0 > 5.8149999999999995)
Predict: -0.3308347482908286
Else (feature 0 > 29.455)
If (feature 1 <= 68.28999999999999)
If (feature 2 <= 1009.875)
Predict: -8.941194411728706
Else (feature 2 > 1009.875)
Predict: -1.8834474815204216
Else (feature 1 > 68.28999999999999)
If (feature 2 <= 1014.405)
Predict: -0.5760094442636617
Else (feature 2 > 1014.405)
Predict: -5.50575933697771
Tree 10 (weight 0.1):
If (feature 2 <= 1005.345)
If (feature 0 <= 27.355)
If (feature 3 <= 99.3)
Predict: -0.5544332197918127
Else (feature 3 > 99.3)
Predict: -6.049841121821514
Else (feature 0 > 27.355)
If (feature 1 <= 70.815)
Predict: -8.03479585317622
Else (feature 1 > 70.815)
Predict: 0.0619269945107018
Else (feature 2 > 1005.345)
If (feature 3 <= 72.41499999999999)
If (feature 0 <= 24.755000000000003)
Predict: 2.020104454165069
Else (feature 0 > 24.755000000000003)
Predict: -0.22964512858748945
Else (feature 3 > 72.41499999999999)
If (feature 1 <= 40.7)
Predict: 1.2987210163143512
Else (feature 1 > 40.7)
Predict: -0.8347660227231797
Tree 11 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 1 <= 42.3)
If (feature 1 <= 39.765)
Predict: 4.261094449524424
Else (feature 1 > 39.765)
Predict: 9.140536590115639
Else (feature 1 > 42.3)
If (feature 2 <= 1007.835)
Predict: 0.9746298984436711
Else (feature 2 > 1007.835)
Predict: -6.317973546286112
Else (feature 0 > 5.0600000000000005)
If (feature 3 <= 95.68)
If (feature 2 <= 1009.295)
Predict: -0.8626626541525325
Else (feature 2 > 1009.295)
Predict: 0.3906915507169364
Else (feature 3 > 95.68)
If (feature 0 <= 11.885000000000002)
Predict: -6.002679638413293
Else (feature 0 > 11.885000000000002)
Predict: -0.7509189525586508
Tree 12 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 51.905)
If (feature 0 <= 13.695)
Predict: 0.5958576966099496
Else (feature 0 > 13.695)
Predict: -1.2694566117501789
Else (feature 1 > 51.905)
If (feature 1 <= 58.19)
Predict: -4.248373100253223
Else (feature 1 > 58.19)
Predict: -9.144729000689791
Else (feature 0 > 18.595)
If (feature 1 <= 47.64)
If (feature 2 <= 1012.675)
Predict: 0.8560563666774771
Else (feature 2 > 1012.675)
Predict: 10.801233083620462
Else (feature 1 > 47.64)
If (feature 0 <= 20.015)
Predict: 3.1978561830060213
Else (feature 0 > 20.015)
Predict: -0.16716555702980626
Tree 13 (weight 0.1):
If (feature 0 <= 28.655)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 58.19)
Predict: 0.34765099851241454
Else (feature 1 > 58.19)
Predict: -1.8189329025195076
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 22.525)
Predict: 6.490484815153365
Else (feature 0 > 22.525)
Predict: 0.7933381751314565
Else (feature 0 > 28.655)
If (feature 1 <= 68.28999999999999)
If (feature 2 <= 1005.665)
Predict: -8.922459399148678
Else (feature 2 > 1005.665)
Predict: -2.3989441108132668
Else (feature 1 > 68.28999999999999)
If (feature 2 <= 1018.215)
Predict: -0.3023338438804105
Else (feature 2 > 1018.215)
Predict: 14.559949112833806
Tree 14 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
If (feature 0 <= 11.885000000000002)
Predict: -0.8897676968273979
Else (feature 0 > 11.885000000000002)
Predict: 3.5874467728768487
Else (feature 0 > 13.695)
If (feature 1 <= 46.345)
Predict: -3.2690852418064273
Else (feature 1 > 46.345)
Predict: -8.844433418899179
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 43.175)
If (feature 1 <= 41.805)
Predict: 3.298855216614116
Else (feature 1 > 41.805)
Predict: 9.150659154207167
Else (feature 1 > 43.175)
If (feature 2 <= 1012.495)
Predict: -0.7109832273625656
Else (feature 2 > 1012.495)
Predict: 1.1631179843236674
Tree 15 (weight 0.1):
If (feature 3 <= 90.055)
If (feature 0 <= 30.41)
If (feature 1 <= 66.21000000000001)
Predict: -0.01732155577258642
Else (feature 1 > 66.21000000000001)
Predict: 1.3432966941310502
Else (feature 0 > 30.41)
If (feature 1 <= 69.465)
Predict: -3.6657811330207344
Else (feature 1 > 69.465)
Predict: -0.38803561342243853
Else (feature 3 > 90.055)
If (feature 2 <= 1015.725)
If (feature 1 <= 47.64)
Predict: 1.5063080039677825
Else (feature 1 > 47.64)
Predict: -1.860635777865782
Else (feature 2 > 1015.725)
If (feature 1 <= 40.7)
Predict: 0.21921885078759318
Else (feature 1 > 40.7)
Predict: -4.793242614118129
Tree 16 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -1.8477082180199003
Else (feature 1 > 39.620000000000005)
Predict: 16.387935166882745
Else (feature 3 > 67.42500000000001)
If (feature 0 <= 5.0600000000000005)
Predict: 4.152127693563297
Else (feature 0 > 5.0600000000000005)
Predict: 0.7556214745509566
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 2 <= 1022.8399999999999)
Predict: -2.9436476590152036
Else (feature 2 > 1022.8399999999999)
Predict: -8.032234733606465
Else (feature 0 > 8.575)
If (feature 0 <= 9.325)
Predict: 5.082738415532321
Else (feature 0 > 9.325)
Predict: -0.032287806549855816
Tree 17 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 0 <= 28.655)
If (feature 0 <= 11.885000000000002)
Predict: -4.455566502931916
Else (feature 0 > 11.885000000000002)
Predict: 1.9027943448616742
Else (feature 0 > 28.655)
If (feature 1 <= 66.21000000000001)
Predict: -3.1810487244675034
Else (feature 1 > 66.21000000000001)
Predict: 0.11525363261816625
Else (feature 3 > 61.89)
If (feature 1 <= 43.175)
If (feature 0 <= 18.595)
Predict: 0.22262511130066664
Else (feature 0 > 18.595)
Predict: 10.13639446335034
Else (feature 1 > 43.175)
If (feature 0 <= 22.055)
Predict: -1.4775024201129299
Else (feature 0 > 22.055)
Predict: 0.20591189539330298
Tree 18 (weight 0.1):
If (feature 2 <= 1005.345)
If (feature 1 <= 70.815)
If (feature 0 <= 23.564999999999998)
Predict: -0.178777362918765
Else (feature 0 > 23.564999999999998)
Predict: -4.52113806132068
Else (feature 1 > 70.815)
If (feature 3 <= 60.025000000000006)
Predict: 4.247605743793766
Else (feature 3 > 60.025000000000006)
Predict: -0.26865379510738685
Else (feature 2 > 1005.345)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 55.825)
Predict: 0.3773109266288433
Else (feature 1 > 55.825)
Predict: -1.3659380946007862
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 21.335)
Predict: 8.448170645066464
Else (feature 0 > 21.335)
Predict: 0.5048679905700459
Tree 19 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 51.905)
If (feature 0 <= 17.655)
Predict: -0.003054757854134698
Else (feature 0 > 17.655)
Predict: -2.9290357116654935
Else (feature 1 > 51.905)
If (feature 1 <= 66.525)
Predict: -4.061825439604536
Else (feature 1 > 66.525)
Predict: -11.67844591879286
Else (feature 0 > 18.595)
If (feature 0 <= 23.945)
If (feature 3 <= 74.525)
Predict: 3.807909998319029
Else (feature 3 > 74.525)
Predict: 0.008459259251505081
Else (feature 0 > 23.945)
If (feature 1 <= 74.915)
Predict: -0.6429811796826012
Else (feature 1 > 74.915)
Predict: 2.099670946504916
Tree 20 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 14.285)
If (feature 0 <= 11.885000000000002)
Predict: -0.7234702732306101
Else (feature 0 > 11.885000000000002)
Predict: 1.589299673192479
Else (feature 0 > 14.285)
If (feature 2 <= 1016.495)
Predict: -2.4381805412578545
Else (feature 2 > 1016.495)
Predict: -6.544687605774689
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 43.005)
If (feature 2 <= 1008.405)
Predict: 0.8119248626873221
Else (feature 2 > 1008.405)
Predict: 5.506769369239865
Else (feature 1 > 43.005)
If (feature 2 <= 1012.935)
Predict: -0.5028175384892806
Else (feature 2 > 1012.935)
Predict: 1.0290531238165197
Tree 21 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.9752219334588972
Else (feature 1 > 39.620000000000005)
Predict: 13.369575512848845
Else (feature 3 > 67.42500000000001)
If (feature 1 <= 42.705)
Predict: 1.850492181024754
Else (feature 1 > 42.705)
Predict: -2.033710059657357
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 2 <= 1022.8399999999999)
Predict: -2.256120115332435
Else (feature 2 > 1022.8399999999999)
Predict: -6.380948299395909
Else (feature 0 > 8.575)
If (feature 0 <= 9.594999999999999)
Predict: 3.4448565562285904
Else (feature 0 > 9.594999999999999)
Predict: -0.05858832726578875
Tree 22 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 21.095)
If (feature 1 <= 66.21000000000001)
Predict: -0.17987193554240402
Else (feature 1 > 66.21000000000001)
Predict: 6.044335675519052
Else (feature 0 > 21.095)
If (feature 1 <= 66.21000000000001)
Predict: -5.809637597188877
Else (feature 1 > 66.21000000000001)
Predict: 2.7761443044302214
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
If (feature 1 <= 64.74000000000001)
Predict: 5.951769778696803
Else (feature 1 > 64.74000000000001)
Predict: -0.30197045172071896
Else (feature 0 > 23.564999999999998)
If (feature 1 <= 44.519999999999996)
Predict: -7.283292958317056
Else (feature 1 > 44.519999999999996)
Predict: -0.19387573131258415
Tree 23 (weight 0.1):
If (feature 3 <= 53.025000000000006)
If (feature 0 <= 24.945)
If (feature 0 <= 18.29)
Predict: 1.0381040338364682
Else (feature 0 > 18.29)
Predict: 5.164469183375362
Else (feature 0 > 24.945)
If (feature 1 <= 66.21000000000001)
Predict: -1.0707595335847206
Else (feature 1 > 66.21000000000001)
Predict: 0.8108674466901084
Else (feature 3 > 53.025000000000006)
If (feature 1 <= 73.725)
If (feature 1 <= 71.505)
Predict: -0.0525992725981239
Else (feature 1 > 71.505)
Predict: -2.807221561152817
Else (feature 1 > 73.725)
If (feature 2 <= 1017.295)
Predict: 1.037788102485777
Else (feature 2 > 1017.295)
Predict: 7.287181806639116
Tree 24 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 70.815)
If (feature 1 <= 59.144999999999996)
Predict: -0.3743851611431973
Else (feature 1 > 59.144999999999996)
Predict: -4.648812647772385
Else (feature 1 > 70.815)
If (feature 0 <= 27.884999999999998)
Predict: 2.4545579195950995
Else (feature 0 > 27.884999999999998)
Predict: -0.32777974793969294
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
Predict: 0.045218490740101897
Else (feature 0 > 20.795)
Predict: -3.3620385127109333
Else (feature 0 > 22.055)
If (feature 0 <= 22.955)
Predict: 4.485324626081587
Else (feature 0 > 22.955)
Predict: 0.13166549369330485
Tree 25 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 58.19)
If (feature 0 <= 17.655)
Predict: -0.023636417468335308
Else (feature 0 > 17.655)
Predict: -2.607492744918568
Else (feature 1 > 58.19)
If (feature 1 <= 66.525)
Predict: -4.8994505786627665
Else (feature 1 > 66.525)
Predict: -10.07085237281708
Else (feature 0 > 18.595)
If (feature 0 <= 19.725)
If (feature 1 <= 66.21000000000001)
Predict: 2.2716539497960406
Else (feature 1 > 66.21000000000001)
Predict: 13.879740983230867
Else (feature 0 > 19.725)
If (feature 1 <= 43.005)
Predict: 5.616411926544602
Else (feature 1 > 43.005)
Predict: -0.00532316539213451
Tree 26 (weight 0.1):
If (feature 3 <= 93.075)
If (feature 1 <= 55.825)
If (feature 0 <= 22.055)
Predict: 0.15441544139943786
Else (feature 0 > 22.055)
Predict: 2.7626508552722306
Else (feature 1 > 55.825)
If (feature 1 <= 66.21000000000001)
Predict: -1.2060290652635515
Else (feature 1 > 66.21000000000001)
Predict: 0.4827535356971057
Else (feature 3 > 93.075)
If (feature 2 <= 1015.725)
If (feature 0 <= 7.34)
Predict: 4.241421869391143
Else (feature 0 > 7.34)
Predict: -0.704916847045625
Else (feature 2 > 1015.725)
If (feature 0 <= 11.885000000000002)
Predict: -5.4904249010577795
Else (feature 0 > 11.885000000000002)
Predict: 0.0976872424106726
Tree 27 (weight 0.1):
If (feature 2 <= 1008.745)
If (feature 1 <= 41.665)
If (feature 1 <= 39.765)
Predict: -1.0511879426704696
Else (feature 1 > 39.765)
Predict: 1.894883627758776
Else (feature 1 > 41.665)
If (feature 1 <= 41.805)
Predict: -13.576686832482961
Else (feature 1 > 41.805)
Predict: -0.7168167899342832
Else (feature 2 > 1008.745)
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
Predict: 0.303140697564446
Else (feature 0 > 13.695)
Predict: -2.8034862119109945
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 55.825)
Predict: 2.0736158373993576
Else (feature 1 > 55.825)
Predict: -0.03336709818500243
Tree 28 (weight 0.1):
If (feature 2 <= 1001.785)
If (feature 3 <= 69.86500000000001)
If (feature 1 <= 69.465)
Predict: -7.299995136853619
Else (feature 1 > 69.465)
Predict: 0.23757420536270835
Else (feature 3 > 69.86500000000001)
If (feature 0 <= 7.34)
Predict: 3.4313360513286284
Else (feature 0 > 7.34)
Predict: -1.276734468456009
Else (feature 2 > 1001.785)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 11.335)
Predict: 0.04499264982858887
Else (feature 0 > 11.335)
Predict: -6.156735713959919
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 12.715)
Predict: 4.4864210547378915
Else (feature 0 > 12.715)
Predict: 0.030721690290287165
Tree 29 (weight 0.1):
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 26.505000000000003)
If (feature 0 <= 22.055)
Predict: -0.3694472776628706
Else (feature 0 > 22.055)
Predict: 1.5208925945902059
Else (feature 0 > 26.505000000000003)
If (feature 1 <= 43.510000000000005)
Predict: -17.99975152056241
Else (feature 1 > 43.510000000000005)
Predict: -2.4033598663496183
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 22.055)
If (feature 0 <= 18.595)
Predict: -7.600012529438
Else (feature 0 > 18.595)
Predict: 6.469471961408998
Else (feature 0 > 22.055)
If (feature 0 <= 25.325)
Predict: -2.6540186758683166
Else (feature 0 > 25.325)
Predict: 0.9869775581610103
Tree 30 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 1 <= 42.3)
If (feature 3 <= 95.68)
Predict: 2.6307256050737506
Else (feature 3 > 95.68)
Predict: 7.168878406232186
Else (feature 1 > 42.3)
If (feature 2 <= 1007.835)
Predict: -0.1884983669158752
Else (feature 2 > 1007.835)
Predict: -5.920088632100639
Else (feature 0 > 5.0600000000000005)
If (feature 1 <= 37.815)
If (feature 0 <= 11.885000000000002)
Predict: -3.8096010917307517
Else (feature 0 > 11.885000000000002)
Predict: 3.5943708074284917
Else (feature 1 > 37.815)
If (feature 1 <= 41.519999999999996)
Predict: 0.6752927073561888
Else (feature 1 > 41.519999999999996)
Predict: -0.14342050966250913
Tree 31 (weight 0.1):
If (feature 3 <= 99.3)
If (feature 0 <= 31.155)
If (feature 3 <= 46.92)
Predict: 2.011051499336945
Else (feature 3 > 46.92)
Predict: 0.012791339921714258
Else (feature 0 > 31.155)
If (feature 2 <= 1014.405)
Predict: -0.916397796921109
Else (feature 2 > 1014.405)
Predict: -6.832504867736499
Else (feature 3 > 99.3)
If (feature 1 <= 39.765)
If (feature 1 <= 38.41)
Predict: -5.505405887296888
Else (feature 1 > 38.41)
Predict: -16.748187635942333
Else (feature 1 > 39.765)
If (feature 0 <= 16.04)
Predict: 0.7708563952983728
Else (feature 0 > 16.04)
Predict: -3.909609799859729
Tree 32 (weight 0.1):
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 64.74000000000001)
If (feature 2 <= 1011.515)
Predict: -1.0002623040885374
Else (feature 2 > 1011.515)
Predict: 0.3591184679910596
Else (feature 1 > 64.74000000000001)
If (feature 2 <= 1006.775)
Predict: -13.788703659238209
Else (feature 2 > 1006.775)
Predict: -2.4620285364725656
Else (feature 1 > 66.21000000000001)
If (feature 1 <= 69.09)
If (feature 0 <= 22.525)
Predict: 6.088217885246919
Else (feature 0 > 22.525)
Predict: 1.0364537580784474
Else (feature 1 > 69.09)
If (feature 0 <= 25.325)
Predict: -2.7946704533493856
Else (feature 0 > 25.325)
Predict: 0.6607665941219878
Tree 33 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 0 <= 29.455)
If (feature 1 <= 66.21000000000001)
Predict: -0.08158010506593574
Else (feature 1 > 66.21000000000001)
Predict: 0.8815313937000332
Else (feature 0 > 29.455)
If (feature 1 <= 44.519999999999996)
Predict: -12.581939252812768
Else (feature 1 > 44.519999999999996)
Predict: -0.77008278158028
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 0.6766771966451415
Else (feature 1 > 39.45)
Predict: -8.445008186341592
Else (feature 0 > 8.575)
If (feature 0 <= 8.96)
Predict: 3.777292703648982
Else (feature 0 > 8.96)
Predict: -2.398045803854934
Tree 34 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.6804590872791323
Else (feature 1 > 39.620000000000005)
Predict: 10.518093452986212
Else (feature 3 > 67.42500000000001)
If (feature 1 <= 42.705)
Predict: 1.3098801739379287
Else (feature 1 > 42.705)
Predict: -1.7450883352732074
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 0 <= 7.765)
Predict: -0.7816329288490581
Else (feature 0 > 7.765)
Predict: -3.7319393815256547
Else (feature 0 > 8.575)
If (feature 0 <= 9.965)
Predict: 2.5600814387681337
Else (feature 0 > 9.965)
Predict: -0.06944896856946733
Tree 35 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 1 <= 39.34)
If (feature 0 <= 16.695)
Predict: -4.078381955056753
Else (feature 0 > 16.695)
Predict: -16.616821684050763
Else (feature 1 > 39.34)
If (feature 1 <= 40.82)
Predict: 4.4394084324272045
Else (feature 1 > 40.82)
Predict: 0.4663514281701948
Else (feature 3 > 61.89)
If (feature 1 <= 58.19)
If (feature 0 <= 22.055)
Predict: -0.027831559557693095
Else (feature 0 > 22.055)
Predict: 2.492136574233702
Else (feature 1 > 58.19)
If (feature 0 <= 18.595)
Predict: -4.183073089901298
Else (feature 0 > 18.595)
Predict: -0.40866909936948326
Tree 36 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 38.754999999999995)
If (feature 3 <= 77.195)
Predict: 9.579430817769946
Else (feature 3 > 77.195)
Predict: 2.6305173165509195
Else (feature 1 > 38.754999999999995)
If (feature 1 <= 70.065)
Predict: -1.801035005150415
Else (feature 1 > 70.065)
Predict: 0.4054557218074593
Else (feature 2 > 1004.505)
If (feature 2 <= 1017.475)
If (feature 1 <= 43.175)
Predict: 1.09341093192285
Else (feature 1 > 43.175)
Predict: -0.03229585395374685
Else (feature 2 > 1017.475)
If (feature 2 <= 1019.365)
Predict: -1.7719467091167656
Else (feature 2 > 1019.365)
Predict: 0.2537098831339789
Tree 37 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
If (feature 0 <= 11.885000000000002)
Predict: -0.5104239863875834
Else (feature 0 > 11.885000000000002)
Predict: 2.1202163252308432
Else (feature 0 > 13.695)
If (feature 3 <= 78.38499999999999)
Predict: -3.379040627299855
Else (feature 3 > 78.38499999999999)
Predict: -0.9911440360990582
Else (feature 0 > 15.475000000000001)
If (feature 0 <= 16.395)
If (feature 3 <= 67.42500000000001)
Predict: 0.4887402058080506
Else (feature 3 > 67.42500000000001)
Predict: 3.884289185845989
Else (feature 0 > 16.395)
If (feature 2 <= 1020.565)
Predict: -0.02862374251288089
Else (feature 2 > 1020.565)
Predict: 3.15734851002617
Tree 38 (weight 0.1):
If (feature 3 <= 43.385)
If (feature 0 <= 24.945)
If (feature 2 <= 1014.405)
Predict: 1.603183026814036
Else (feature 2 > 1014.405)
Predict: 7.05673098720603
Else (feature 0 > 24.945)
If (feature 0 <= 26.295)
Predict: -5.886157652325024
Else (feature 0 > 26.295)
Predict: 0.7387886738447553
Else (feature 3 > 43.385)
If (feature 1 <= 73.725)
If (feature 1 <= 71.505)
Predict: 0.008754170391068655
Else (feature 1 > 71.505)
Predict: -2.0178119354056765
Else (feature 1 > 73.725)
If (feature 1 <= 77.42)
Predict: 1.8396223170900134
Else (feature 1 > 77.42)
Predict: -1.5747478023010713
Tree 39 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
If (feature 0 <= 18.595)
Predict: -0.25201916420658693
Else (feature 0 > 18.595)
Predict: 1.5718496558845116
Else (feature 0 > 20.795)
If (feature 1 <= 66.21000000000001)
Predict: -3.7201562729508804
Else (feature 1 > 66.21000000000001)
Predict: 2.087916157719636
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
If (feature 2 <= 1012.015)
Predict: 0.7371264627237751
Else (feature 2 > 1012.015)
Predict: 4.6381746481309065
Else (feature 0 > 23.564999999999998)
If (feature 1 <= 44.519999999999996)
Predict: -5.772062593218147
Else (feature 1 > 44.519999999999996)
Predict: -0.12977023255758624
Tree 40 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 3 <= 95.68)
If (feature 2 <= 1011.105)
Predict: -3.365769010003795
Else (feature 2 > 1011.105)
Predict: 2.955050770650336
Else (feature 3 > 95.68)
If (feature 2 <= 1008.205)
Predict: 2.728200788704399
Else (feature 2 > 1008.205)
Predict: 6.919382935391042
Else (feature 0 > 5.0600000000000005)
If (feature 3 <= 96.61)
If (feature 1 <= 37.815)
Predict: -1.3949762323292278
Else (feature 1 > 37.815)
Predict: 0.06411431759031856
Else (feature 3 > 96.61)
If (feature 0 <= 11.605)
Predict: -3.491961189446582
Else (feature 0 > 11.605)
Predict: -0.16406940197018208
Tree 41 (weight 0.1):
If (feature 0 <= 30.41)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 58.85)
Predict: 0.15540001609212736
Else (feature 1 > 58.85)
Predict: -1.1352706510871309
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: -0.6390388689881783
Else (feature 0 > 25.325)
Predict: 1.3909471658872326
Else (feature 0 > 30.41)
If (feature 2 <= 1014.405)
If (feature 1 <= 56.894999999999996)
Predict: -9.841212730443857
Else (feature 1 > 56.894999999999996)
Predict: -0.5054201979116707
Else (feature 2 > 1014.405)
If (feature 1 <= 74.915)
Predict: -5.898616989217175
Else (feature 1 > 74.915)
Predict: 1.042314191558674
Tree 42 (weight 0.1):
If (feature 2 <= 1009.625)
If (feature 1 <= 43.005)
If (feature 1 <= 42.025000000000006)
Predict: -0.05077288158998095
Else (feature 1 > 42.025000000000006)
Predict: 3.619244461816007
Else (feature 1 > 43.005)
If (feature 0 <= 18.595)
Predict: -3.5057172058309307
Else (feature 0 > 18.595)
Predict: -0.32537979322253646
Else (feature 2 > 1009.625)
If (feature 2 <= 1017.475)
If (feature 1 <= 55.825)
Predict: 0.8076710222538108
Else (feature 1 > 55.825)
Predict: -0.01784249647067053
Else (feature 2 > 1017.475)
If (feature 2 <= 1019.365)
Predict: -1.4312272708033218
Else (feature 2 > 1019.365)
Predict: 0.20844195286029432
Tree 43 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 0 <= 17.655)
If (feature 0 <= 15.475000000000001)
Predict: -0.2913115404147692
Else (feature 0 > 15.475000000000001)
Predict: 1.1145997107947745
Else (feature 0 > 17.655)
If (feature 1 <= 37.815)
Predict: 14.538914074443028
Else (feature 1 > 37.815)
Predict: -3.0407663665320683
Else (feature 0 > 18.595)
If (feature 2 <= 1020.325)
If (feature 1 <= 43.175)
Predict: 3.881117973012625
Else (feature 1 > 43.175)
Predict: 0.04538590552170286
Else (feature 2 > 1020.325)
If (feature 1 <= 64.74000000000001)
Predict: 4.662857871344832
Else (feature 1 > 64.74000000000001)
Predict: -43.5190245896606
Tree 44 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.7153417257252007
Else (feature 1 > 39.620000000000005)
Predict: 8.417772324738223
Else (feature 3 > 67.42500000000001)
If (feature 2 <= 1010.985)
Predict: 2.5111312207852263
Else (feature 2 > 1010.985)
Predict: 0.29950092637128345
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 1 <= 40.06)
Predict: -3.30672576579827
Else (feature 1 > 40.06)
Predict: -0.45097750406116727
Else (feature 0 > 8.575)
If (feature 0 <= 9.325)
Predict: 3.226884779601992
Else (feature 0 > 9.325)
Predict: -0.041218673012550146
Tree 45 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 0 <= 10.395)
If (feature 3 <= 92.22999999999999)
Predict: 1.0355113706204075
Else (feature 3 > 92.22999999999999)
Predict: -1.4603765116034264
Else (feature 0 > 10.395)
If (feature 0 <= 11.885000000000002)
Predict: -2.630098257038007
Else (feature 0 > 11.885000000000002)
Predict: 0.09582680902226459
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 1.107359449029309
Else (feature 1 > 39.45)
Predict: -6.096185488604492
Else (feature 0 > 8.575)
If (feature 3 <= 70.57499999999999)
Predict: -3.516006231223605
Else (feature 3 > 70.57499999999999)
Predict: -0.26144006873351155
Tree 46 (weight 0.1):
If (feature 0 <= 27.884999999999998)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 25.145)
Predict: 0.06412914679730906
Else (feature 0 > 25.145)
Predict: -1.554041618425865
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: -0.4748636523867588
Else (feature 0 > 25.325)
Predict: 2.4595627925276826
Else (feature 0 > 27.884999999999998)
If (feature 3 <= 61.89)
If (feature 1 <= 71.895)
Predict: -0.4429995884239883
Else (feature 1 > 71.895)
Predict: 1.4321627506429122
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
Predict: -0.2617373838209719
Else (feature 1 > 71.505)
Predict: -2.637373068367584
Tree 47 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 1 <= 39.34)
If (feature 0 <= 16.695)
Predict: -2.855821258258964
Else (feature 0 > 16.695)
Predict: -13.80709248590955
Else (feature 1 > 39.34)
If (feature 1 <= 74.915)
Predict: 0.35443616318343385
Else (feature 1 > 74.915)
Predict: 2.9400682899293233
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
If (feature 1 <= 66.85499999999999)
Predict: -0.142107366817925
Else (feature 1 > 66.85499999999999)
Predict: 1.0352328327059535
Else (feature 1 > 71.505)
If (feature 1 <= 73.00999999999999)
Predict: -2.947898208728739
Else (feature 1 > 73.00999999999999)
Predict: -0.0830706492691125
Tree 48 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 38.754999999999995)
If (feature 3 <= 86.625)
Predict: 4.921331281961159
Else (feature 3 > 86.625)
Predict: -5.961237317667383
Else (feature 1 > 38.754999999999995)
If (feature 1 <= 39.765)
Predict: -4.436072018762161
Else (feature 1 > 39.765)
Predict: -0.7283906418193187
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
Predict: 0.044899959208437666
Else (feature 0 > 20.795)
Predict: -2.2295677836603995
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
Predict: 2.3559950404053414
Else (feature 0 > 23.564999999999998)
Predict: -0.06950420285239005
Tree 49 (weight 0.1):
If (feature 1 <= 41.435)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 11.335)
Predict: -0.02697483446966382
Else (feature 0 > 11.335)
Predict: -3.7031660865730367
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 13.265)
Predict: 4.209724879257512
Else (feature 0 > 13.265)
Predict: 0.41602586770224464
Else (feature 1 > 41.435)
If (feature 1 <= 41.805)
If (feature 3 <= 89.595)
Predict: -1.6099299736449546
Else (feature 3 > 89.595)
Predict: -9.419799062932288
Else (feature 1 > 41.805)
If (feature 1 <= 43.175)
Predict: 1.5951066240527068
Else (feature 1 > 43.175)
Predict: -0.11481901338423915
Tree 50 (weight 0.1):
If (feature 1 <= 38.41)
If (feature 0 <= 11.885000000000002)
If (feature 2 <= 1016.665)
Predict: -5.344526613859499
Else (feature 2 > 1016.665)
Predict: 0.058134490545794976
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 16.985)
Predict: 1.537246393097642
Else (feature 0 > 16.985)
Predict: 9.920778277565365
Else (feature 1 > 38.41)
If (feature 0 <= 13.485)
If (feature 1 <= 51.045)
Predict: 0.6675749827847329
Else (feature 1 > 51.045)
Predict: -5.737026170963195
Else (feature 0 > 13.485)
If (feature 0 <= 15.475000000000001)
Predict: -1.7210713764803844
Else (feature 0 > 15.475000000000001)
Predict: 0.09027853735147576
Tree 51 (weight 0.1):
If (feature 0 <= 31.155)
If (feature 3 <= 48.230000000000004)
If (feature 2 <= 1020.325)
Predict: 0.9307527734280435
Else (feature 2 > 1020.325)
Predict: 9.070547343579028
Else (feature 3 > 48.230000000000004)
If (feature 1 <= 73.725)
Predict: -0.06359121142988887
Else (feature 1 > 73.725)
Predict: 1.049949579330099
Else (feature 0 > 31.155)
If (feature 1 <= 63.08)
If (feature 1 <= 44.519999999999996)
Predict: -6.355864033256125
Else (feature 1 > 44.519999999999996)
Predict: 13.6760008529786
Else (feature 1 > 63.08)
If (feature 1 <= 68.28999999999999)
Predict: -3.953494984806905
Else (feature 1 > 68.28999999999999)
Predict: -0.6145841300086397
Tree 52 (weight 0.1):
If (feature 2 <= 1012.495)
If (feature 1 <= 41.435)
If (feature 1 <= 39.975)
Predict: -0.4047468839922722
Else (feature 1 > 39.975)
Predict: 2.509624688414308
Else (feature 1 > 41.435)
If (feature 1 <= 41.805)
Predict: -6.811044477667833
Else (feature 1 > 41.805)
Predict: -0.26889174065489546
Else (feature 2 > 1012.495)
If (feature 3 <= 92.22999999999999)
If (feature 1 <= 58.19)
Predict: 0.7048711166950787
Else (feature 1 > 58.19)
Predict: -0.5475390646726656
Else (feature 3 > 92.22999999999999)
If (feature 1 <= 41.805)
Predict: -3.7013459723577355
Else (feature 1 > 41.805)
Predict: 0.7237930378019226
Tree 53 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 0 <= 17.335)
If (feature 3 <= 74.08500000000001)
Predict: -0.8631764946312587
Else (feature 3 > 74.08500000000001)
Predict: 0.2803856631344212
Else (feature 0 > 17.335)
If (feature 1 <= 42.705)
Predict: 0.9335711174385192
Else (feature 1 > 42.705)
Predict: -2.950020164379197
Else (feature 0 > 18.595)
If (feature 2 <= 1013.395)
If (feature 1 <= 66.85499999999999)
Predict: -0.7231135633124072
Else (feature 1 > 66.85499999999999)
Predict: 0.41724670068145925
Else (feature 2 > 1013.395)
If (feature 1 <= 58.19)
Predict: 4.568024116358373
Else (feature 1 > 58.19)
Predict: -0.36270787813043714
Tree 54 (weight 0.1):
If (feature 2 <= 1001.785)
If (feature 3 <= 63.575)
If (feature 3 <= 60.025000000000006)
Predict: -1.7427137986580719
Else (feature 3 > 60.025000000000006)
Predict: -7.776342039375448
Else (feature 3 > 63.575)
If (feature 0 <= 27.119999999999997)
Predict: 0.026174663094801796
Else (feature 0 > 27.119999999999997)
Predict: -2.343138620856655
Else (feature 2 > 1001.785)
If (feature 0 <= 22.055)
If (feature 1 <= 66.21000000000001)
Predict: -0.27236570115397085
Else (feature 1 > 66.21000000000001)
Predict: 3.164590339421908
Else (feature 0 > 22.055)
If (feature 0 <= 22.725)
Predict: 2.9597120242025485
Else (feature 0 > 22.725)
Predict: 0.054881549113388446
Tree 55 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 1 <= 45.754999999999995)
If (feature 0 <= 26.715)
Predict: 0.2599176823406179
Else (feature 0 > 26.715)
Predict: -5.549910372715466
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 15.475000000000001)
Predict: -2.867925531108855
Else (feature 0 > 15.475000000000001)
Predict: -0.0236838100703647
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 0.7777761733424086
Else (feature 1 > 39.45)
Predict: -5.202001886405932
Else (feature 0 > 8.575)
If (feature 3 <= 68.945)
Predict: -3.641368616696809
Else (feature 3 > 68.945)
Predict: -0.6008141057791702
Tree 56 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 0 <= 18.29)
If (feature 2 <= 1019.025)
Predict: -2.1632736300070996
Else (feature 2 > 1019.025)
Predict: 1.2430029950309498
Else (feature 0 > 18.29)
If (feature 1 <= 44.91)
Predict: 3.2134324628571003
Else (feature 1 > 44.91)
Predict: 0.2988556025240279
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
If (feature 1 <= 66.85499999999999)
Predict: -0.09866504525517336
Else (feature 1 > 66.85499999999999)
Predict: 0.7271499788275563
Else (feature 1 > 71.505)
If (feature 1 <= 73.00999999999999)
Predict: -2.395971400707892
Else (feature 1 > 73.00999999999999)
Predict: -0.16608323426526406
Tree 57 (weight 0.1):
If (feature 2 <= 1017.645)
If (feature 2 <= 1014.615)
If (feature 1 <= 43.175)
Predict: 1.0389282601473917
Else (feature 1 > 43.175)
Predict: -0.3646675630154698
Else (feature 2 > 1014.615)
If (feature 1 <= 43.510000000000005)
Predict: -0.6889797697082773
Else (feature 1 > 43.510000000000005)
Predict: 1.5908362409321974
Else (feature 2 > 1017.645)
If (feature 2 <= 1019.025)
If (feature 1 <= 71.18)
Predict: -1.6569133708803008
Else (feature 1 > 71.18)
Predict: 4.314967971455512
Else (feature 2 > 1019.025)
If (feature 3 <= 89.595)
Predict: 0.43765066032139976
Else (feature 3 > 89.595)
Predict: -1.7344432288008194
Tree 58 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 70.815)
If (feature 1 <= 59.834999999999994)
Predict: -0.170418541124326
Else (feature 1 > 59.834999999999994)
Predict: -2.895038840312831
Else (feature 1 > 70.815)
If (feature 2 <= 1000.505)
Predict: -1.3832637115340176
Else (feature 2 > 1000.505)
Predict: 1.2249185299155396
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 1 <= 66.21000000000001)
Predict: -0.2418541439735617
Else (feature 1 > 66.21000000000001)
Predict: 3.0110894160107886
Else (feature 0 > 22.055)
If (feature 0 <= 23.945)
Predict: 1.4999960684883906
Else (feature 0 > 23.945)
Predict: -0.042407486119460915
Tree 59 (weight 0.1):
If (feature 1 <= 77.42)
If (feature 1 <= 74.915)
If (feature 1 <= 71.505)
Predict: 0.03983464447255206
Else (feature 1 > 71.505)
Predict: -0.7683021525819648
Else (feature 1 > 74.915)
If (feature 3 <= 72.82)
Predict: 2.774179202497669
Else (feature 3 > 72.82)
Predict: -0.32979787820368633
Else (feature 1 > 77.42)
If (feature 0 <= 21.595)
If (feature 0 <= 21.335)
Predict: -13.565760771414489
Else (feature 0 > 21.335)
Predict: -13.954507897669714
Else (feature 0 > 21.595)
If (feature 0 <= 22.955)
Predict: 10.853700477067264
Else (feature 0 > 22.955)
Predict: -1.4643467280880287
Tree 60 (weight 0.1):
If (feature 0 <= 10.395)
If (feature 1 <= 40.629999999999995)
If (feature 3 <= 75.545)
Predict: -2.3127871072788375
Else (feature 3 > 75.545)
Predict: 0.3992391857664209
Else (feature 1 > 40.629999999999995)
If (feature 3 <= 89.595)
Predict: 2.9152362525803523
Else (feature 3 > 89.595)
Predict: -1.4086879927580456
Else (feature 0 > 10.395)
If (feature 0 <= 11.885000000000002)
If (feature 2 <= 1025.2150000000001)
Predict: -2.2352340341897547
Else (feature 2 > 1025.2150000000001)
Predict: 5.952010328542228
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 12.415)
Predict: 3.4957190546300447
Else (feature 0 > 12.415)
Predict: -0.03992973893781255
Tree 61 (weight 0.1):
If (feature 1 <= 40.82)
If (feature 1 <= 40.224999999999994)
If (feature 3 <= 75.545)
Predict: -1.2944988036912455
Else (feature 3 > 75.545)
Predict: 0.44866615475817667
Else (feature 1 > 40.224999999999994)
If (feature 2 <= 1025.2150000000001)
Predict: 0.7826360308981203
Else (feature 2 > 1025.2150000000001)
Predict: 9.875672812487991
Else (feature 1 > 40.82)
If (feature 2 <= 1025.2150000000001)
If (feature 0 <= 10.945)
Predict: 1.1628421208118285
Else (feature 0 > 10.945)
Predict: -0.12551627485602937
Else (feature 2 > 1025.2150000000001)
If (feature 1 <= 41.15)
Predict: -7.782369290305258
Else (feature 1 > 41.15)
Predict: -1.200273276366743
Tree 62 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 21.335)
If (feature 1 <= 66.21000000000001)
Predict: -0.09621281022933233
Else (feature 1 > 66.21000000000001)
Predict: 2.680549669942832
Else (feature 0 > 21.335)
If (feature 1 <= 63.864999999999995)
Predict: -3.207766197974041
Else (feature 1 > 63.864999999999995)
Predict: -0.14162445042909583
Else (feature 0 > 22.055)
If (feature 0 <= 22.725)
If (feature 2 <= 1011.515)
Predict: 0.5064350504779975
Else (feature 2 > 1011.515)
Predict: 3.659612527058891
Else (feature 0 > 22.725)
If (feature 3 <= 75.545)
Predict: 0.2595183211083464
Else (feature 3 > 75.545)
Predict: -0.6564588168227348
Tree 63 (weight 0.1):
If (feature 2 <= 1017.645)
If (feature 1 <= 44.67)
If (feature 1 <= 44.13)
Predict: 0.3869693354075677
Else (feature 1 > 44.13)
Predict: 4.723132901950317
Else (f...
Conclusion
Wow! So our best model is in fact our Gradient Boosted Decision tree model which uses an ensemble of 120 Trees with a depth of 3 to construct a better model than the single decision tree.
Step 8: Deployment will be done later
Now that we have a predictive model it is time to deploy the model into an operational environment.
In our example, let's say we have a series of sensors attached to the power plant and a monitoring station.
The monitoring station will need close to real-time information about how much power that their station will generate so they can relay that to the utility.
For this we need to create a Spark Streaming utility that we can use for this purpose. For this you need to be introduced to basic concepts in Spark Streaming first. See http://spark.apache.org/docs/latest/streaming-programming-guide.html if you can't wait!
After deployment you will be able to use the best predictions from gradient boosed regression trees to feed a real-time dashboard or feed the utility with information on how much power the peaker plant will deliver give current conditions.
Persisting Statistical Machine Learning Models
See https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html
Let's save our best model so we can load it without having to rerun the validation and training again.
gbtModel
res117: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
gbtModel.bestModel.asInstanceOf[PipelineModel]
.write.overwrite().save("/databricks/driver/MyTrainedBestPipelineModel")
When it is time to deploy the trained model to serve predicitons, we can simply reload this model and proceed as shown later.
This is one of the Deploymnet Patterns for Serving a Model's Prediction.
Datasource References:
- Pinar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615, Web Link
- Heysem Kaya, Pinar Tüfekci , Sadik Fikret Gürgen: Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai) Web Link
Activity Recognition from Accelerometer
- ETL Using SparkSQL Windows
- Prediction using Random Forest
This work is a simpler databricksification of Amira Lakhal's more complex framework for activity recognition:
See Section below on 0. Download and Load Data first.
- Once data is loaded come back here (if you have not downloaded and loaded data into the distributed file system under your hood).
val data = sc.textFile("/datasets/sds/ActivityRecognition/dataTraining.csv") // assumes data is loaded
data: org.apache.spark.rdd.RDD[String] = /datasets/sds/ActivityRecognition/dataTraining.csv MapPartitionsRDD[256] at textFile at command-2971213210275754:1
data.take(5).foreach(println)
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
val dataDF = sqlContext.read
.format("com.databricks.spark.csv") // use spark.csv package
.option("header", "true") // Use first line of all files as header
.option("inferSchema", "true") // Automatically infer data types
.option("delimiter", ",") // Specify the delimiter as ','
.load("/datasets/sds/ActivityRecognition/dataTraining.csv")
dataDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
val dataDFnew = spark.read.format("csv")
.option("inferSchema", "true")
.option("header", "true")
.option("sep", ",")
.load("/datasets/sds/ActivityRecognition/dataTraining.csv")
dataDFnew: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
dataDFnew.printSchema()
root
|-- user_id: string (nullable = true)
|-- activity: string (nullable = true)
|-- timeStampAsLong: long (nullable = true)
|-- x: double (nullable = true)
|-- y: double (nullable = true)
|-- z: double (nullable = true)
display(dataDF) // zp.show(dataDF)
| user_id | activity | timeStampAsLong | x | y | z |
|---|---|---|---|---|---|
| user_001 | Jumping | 1.446047227606e12 | 4.33079 | -12.72175 | -3.18118 |
| user_001 | Jumping | 1.446047227671e12 | 0.575403 | -0.727487 | 2.95007 |
| user_001 | Jumping | 1.446047227735e12 | -1.60885 | 3.52607 | -0.1922 |
| user_001 | Jumping | 1.446047227799e12 | 0.690364 | -3.7722e-2 | 1.72382 |
| user_001 | Jumping | 1.446047227865e12 | 3.44943 | -1.68549 | 2.29862 |
| user_001 | Jumping | 1.44604722793e12 | 1.87829 | -1.91542 | 0.880768 |
| user_001 | Jumping | 1.446047227995e12 | 1.57173 | -5.86241 | -3.75599 |
| user_001 | Jumping | 1.446047228059e12 | 3.41111 | -17.93331 | 0.535886 |
| user_001 | Jumping | 1.446047228123e12 | 3.18118 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047228189e12 | 7.85626 | -19.2362 | 0.804128 |
| user_001 | Jumping | 1.446047228253e12 | 1.26517 | -8.85139 | 2.18366 |
| user_001 | Jumping | 1.446047228318e12 | 7.7239e-2 | 1.15021 | 1.53221 |
| user_001 | Jumping | 1.446047228383e12 | 0.230521 | 2.0699 | -1.41845 |
| user_001 | Jumping | 1.446047228447e12 | 0.652044 | -0.497565 | 1.76214 |
| user_001 | Jumping | 1.446047228512e12 | 1.53341 | -0.305964 | 1.41725 |
| user_001 | Jumping | 1.446047228578e12 | -1.07237 | -1.95374 | 0.191003 |
| user_001 | Jumping | 1.446047228642e12 | 2.75966 | -13.75639 | 0.191003 |
| user_001 | Jumping | 1.446047228707e12 | 7.43474 | -19.58108 | 2.95007 |
| user_001 | Jumping | 1.446047228771e12 | 5.63368 | -19.58108 | 5.59417 |
| user_001 | Jumping | 1.446047228836e12 | 5.02056 | -11.72542 | -1.38013 |
| user_001 | Jumping | 1.4460472289e12 | -2.10702 | 0.575403 | 1.07237 |
| user_001 | Jumping | 1.446047228966e12 | -1.30229 | 2.2615 | -1.26517 |
| user_001 | Jumping | 1.44604722903e12 | 1.68669 | -0.957409 | 1.57053 |
| user_001 | Jumping | 1.446047229095e12 | 2.60638 | -0.229323 | 2.14534 |
| user_001 | Jumping | 1.446047229159e12 | 1.30349 | -0.152682 | 0.497565 |
| user_001 | Jumping | 1.446047229224e12 | 1.64837 | -8.12331 | 1.60885 |
| user_001 | Jumping | 1.44604722929e12 | 0.15388 | -18.46979 | -1.03525 |
| user_001 | Jumping | 1.446047229354e12 | 4.98224 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047229419e12 | 5.17384 | -19.2362 | 0.612526 |
| user_001 | Jumping | 1.446047229483e12 | 2.03158 | -9.11964 | 1.34061 |
| user_001 | Jumping | 1.446047229549e12 | -1.95374 | -1.03405 | 0.574206 |
| user_001 | Jumping | 1.446047229612e12 | 0.1922 | 1.30349 | -1.03525 |
| user_001 | Jumping | 1.446047229678e12 | 1.64837 | -0.727487 | -0.307161 |
| user_001 | Jumping | 1.446047229743e12 | 2.56806 | -1.03405 | 0.267643 |
| user_001 | Jumping | 1.446047229806e12 | 1.41845 | -0.305964 | 0.727487 |
| user_001 | Jumping | 1.446047229872e12 | 1.03525 | -6.82042 | 0.957409 |
| user_001 | Jumping | 1.446047229936e12 | 3.94759 | -19.31284 | -1.22685 |
| user_001 | Jumping | 1.446047230001e12 | 6.13185 | -19.58108 | 2.72014 |
| user_001 | Jumping | 1.446047230066e12 | 7.89458 | -16.9753 | 0.229323 |
| user_001 | Jumping | 1.446047230131e12 | 2.6447 | -3.75479 | 0.114362 |
| user_001 | Jumping | 1.446047230195e12 | -2.41358 | 1.91661 | -5.99e-4 |
| user_001 | Jumping | 1.44604723026e12 | -1.95374 | -0.114362 | -0.268841 |
| user_001 | Jumping | 1.446047230325e12 | -0.229323 | -1.95374 | 2.75846 |
| user_001 | Jumping | 1.44604723039e12 | 2.68302 | 0.268841 | 0.535886 |
| user_001 | Jumping | 1.446047230455e12 | 1.15021 | -1.41725 | 0.880768 |
| user_001 | Jumping | 1.446047230519e12 | 2.98958 | -11.53382 | -0.920286 |
| user_001 | Jumping | 1.446047230584e12 | 8.00954 | -19.58108 | -0.1922 |
| user_001 | Jumping | 1.446047230649e12 | 7.31978 | -19.58108 | 2.52854 |
| user_001 | Jumping | 1.446047230713e12 | 5.44208 | -13.56479 | -0.498763 |
| user_001 | Jumping | 1.446047230779e12 | 0.728685 | -0.420925 | 1.68549 |
| user_001 | Jumping | 1.446047230842e12 | 1.18853 | 1.41845 | -2.22318 |
| user_001 | Jumping | 1.446047230907e12 | 0.15388 | -1.80046 | 2.03038 |
| user_001 | Jumping | 1.446047230972e12 | -3.7722e-2 | 1.07357 | -0.958607 |
| user_001 | Jumping | 1.446047231038e12 | 0.230521 | 0.728685 | -0.690364 |
| user_001 | Jumping | 1.446047231102e12 | 0.805325 | -1.14901 | 1.53221 |
| user_001 | Jumping | 1.446047231167e12 | 5.17384 | -9.15796 | -2.91294 |
| user_001 | Jumping | 1.446047231231e12 | 8.00954 | -19.54276 | 1.49389 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 |
| user_001 | Jumping | 1.446047231361e12 | 7.89458 | -18.35483 | 3.71647 |
| user_001 | Jumping | 1.446047231426e12 | 0.537083 | -3.21831 | 0.420925 |
| user_001 | Jumping | 1.44604723149e12 | -1.91542 | 4.10087 | -2.6447 |
| user_001 | Jumping | 1.446047231555e12 | -0.919089 | -0.382604 | 0.114362 |
| user_001 | Jumping | 1.44604723162e12 | 0.613724 | -0.842448 | 1.57053 |
| user_001 | Jumping | 1.446047231684e12 | 1.38013 | -0.459245 | 0.305964 |
| user_001 | Jumping | 1.446047231749e12 | 2.29982 | -1.49389 | -1.26517 |
| user_001 | Jumping | 1.446047231814e12 | 4.94392 | -10.95901 | -0.498763 |
| user_001 | Jumping | 1.446047231878e12 | 3.75599 | -19.27452 | -1.76333 |
| user_001 | Jumping | 1.446047231944e12 | 7.70298 | -19.58108 | -2.95126 |
| user_001 | Jumping | 1.446047232008e12 | 6.55337 | -17.51178 | -1.91661 |
| user_001 | Jumping | 1.446047232073e12 | 2.10822 | -2.03038 | -0.268841 |
| user_001 | Jumping | 1.446047232138e12 | -1.68549 | 3.67935 | -0.881966 |
| user_001 | Jumping | 1.446047232203e12 | 0.15388 | -0.114362 | 7.6042e-2 |
| user_001 | Jumping | 1.446047232267e12 | 2.79798 | -1.95374 | 0.689167 |
| user_001 | Jumping | 1.446047232332e12 | 1.45677 | -0.957409 | 0.420925 |
| user_001 | Jumping | 1.446047232397e12 | 1.18853 | -2.95007 | 0.650847 |
| user_001 | Jumping | 1.446047232462e12 | 3.44943 | -13.87135 | -3.71767 |
| user_001 | Jumping | 1.446047232526e12 | 3.79431 | -19.58108 | -0.345482 |
| user_001 | Jumping | 1.446047232591e12 | 6.09353 | -19.58108 | 2.87342 |
| user_001 | Jumping | 1.446047232655e12 | 4.48408 | -14.3312 | -0.498763 |
| user_001 | Jumping | 1.44604723272e12 | -3.7722e-2 | -1.68549 | 2.6435 |
| user_001 | Jumping | 1.446047232785e12 | 0.613724 | 2.68302 | -2.37646 |
| user_001 | Jumping | 1.44604723285e12 | 0.996927 | -0.497565 | 0.497565 |
| user_001 | Jumping | 1.446047232915e12 | 0.15388 | -0.689167 | 1.34061 |
| user_001 | Jumping | 1.446047232979e12 | 1.07357 | 1.07357 | -2.60638 |
| user_001 | Jumping | 1.446047233044e12 | 2.37646 | -3.29495 | -1.45677 |
| user_001 | Jumping | 1.446047233109e12 | 2.22318 | -16.09393 | 1.37893 |
| user_001 | Jumping | 1.446047233173e12 | 6.82161 | -19.58108 | 3.7722e-2 |
| user_001 | Jumping | 1.446047233238e12 | 8.39275 | -19.54276 | 1.22565 |
| user_001 | Jumping | 1.446047233303e12 | 1.22685 | -9.50284 | -0.307161 |
| user_001 | Jumping | 1.446047233368e12 | 1.83997 | -0.152682 | 1.41725 |
| user_001 | Jumping | 1.446047233432e12 | -0.765807 | 0.996927 | -1.91661 |
| user_001 | Jumping | 1.446047233497e12 | 0.728685 | -0.612526 | 0.152682 |
| user_001 | Jumping | 1.446047233562e12 | 0.996927 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.446047233626e12 | 1.22685 | -0.305964 | 3.7722e-2 |
| user_001 | Jumping | 1.446047233692e12 | 1.91661 | -2.95007 | -0.345482 |
| user_001 | Jumping | 1.446047233755e12 | 4.75232 | -14.63776 | -3.67935 |
| user_001 | Jumping | 1.446047233821e12 | 4.56072 | -19.58108 | -2.6447 |
| user_001 | Jumping | 1.446047233886e12 | 10.69197 | -19.38948 | -5.94025 |
| user_001 | Jumping | 1.44604723395e12 | 3.10454 | -10.42253 | -2.22318 |
| user_001 | Jumping | 1.446047234015e12 | -0.152682 | 1.61005 | 1.80046 |
| user_001 | Jumping | 1.44604723408e12 | -0.267643 | 1.83997 | -0.958607 |
| user_001 | Jumping | 1.446047234145e12 | 1.99325 | -0.842448 | -0.230521 |
| user_001 | Jumping | 1.44604723421e12 | 2.79798 | -0.114362 | 0.919089 |
| user_001 | Jumping | 1.446047234273e12 | 1.11189 | -0.152682 | 1.83878 |
| user_001 | Jumping | 1.446047234339e12 | 2.75966 | -5.05768 | -0.537083 |
| user_001 | Jumping | 1.446047234403e12 | 4.33079 | -16.82202 | -3.06622 |
| user_001 | Jumping | 1.446047234468e12 | 6.89825 | -19.58108 | -4.25415 |
| user_001 | Jumping | 1.446047234533e12 | 11.49669 | -19.50444 | -2.52974 |
| user_001 | Jumping | 1.446047234598e12 | 6.20849 | -9.08132 | -1.80165 |
| user_001 | Jumping | 1.446047234663e12 | 0.805325 | 1.15021 | -0.15388 |
| user_001 | Jumping | 1.446047234726e12 | -1.76214 | 2.14654 | -0.460442 |
| user_001 | Jumping | 1.446047234792e12 | 5.99e-4 | -1.11069 | -0.230521 |
| user_001 | Jumping | 1.446047234857e12 | 0.652044 | 0.230521 | -0.920286 |
| user_001 | Jumping | 1.446047234922e12 | 1.26517 | -0.114362 | -5.99e-4 |
| user_001 | Jumping | 1.446047234986e12 | 5.36544 | -4.09967 | -0.537083 |
| user_001 | Jumping | 1.446047235051e12 | 6.51505 | -17.05194 | -3.56439 |
| user_001 | Jumping | 1.446047235115e12 | 6.93658 | -19.58108 | -2.75966 |
| user_001 | Jumping | 1.446047235181e12 | 7.89458 | -19.58108 | -3.18118 |
| user_001 | Jumping | 1.446047235245e12 | 1.95493 | -9.6178 | -1.34181 |
| user_001 | Jumping | 1.44604723531e12 | -2.18366 | 3.14286 | -0.1922 |
| user_001 | Jumping | 1.446047235375e12 | -2.87342 | 1.99325 | -1.76333 |
| user_001 | Jumping | 1.446047235439e12 | 1.03525 | -1.80046 | 1.64717 |
| user_001 | Jumping | 1.446047235504e12 | 1.95493 | -0.689167 | 1.49389 |
| user_001 | Jumping | 1.446047235569e12 | 1.26517 | 0.383802 | -0.307161 |
| user_001 | Jumping | 1.446047235633e12 | 1.18853 | -2.72014 | 1.41725 |
| user_001 | Jumping | 1.446047235698e12 | 5.71033 | -16.51545 | -2.03158 |
| user_001 | Jumping | 1.446047235763e12 | 6.55337 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047235828e12 | 10.577 | -19.4278 | -1.57173 |
| user_001 | Jumping | 1.446047235892e12 | 2.79798 | -9.88604 | -0.843646 |
| user_001 | Jumping | 1.446047235957e12 | 0.652044 | -3.7722e-2 | 1.14901 |
| user_001 | Jumping | 1.446047236022e12 | 0.843646 | 1.95493 | -2.14654 |
| user_001 | Jumping | 1.446047236086e12 | 0.15388 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.446047236152e12 | 0.767005 | -0.114362 | 0.804128 |
| user_001 | Jumping | 1.446047236215e12 | -0.650847 | -0.650847 | -0.230521 |
| user_001 | Jumping | 1.446047236281e12 | 3.56439 | -5.90073 | 0.727487 |
| user_001 | Jumping | 1.446047236346e12 | 7.05154 | -17.97163 | -1.61005 |
| user_001 | Jumping | 1.44604723641e12 | 9.46572 | -19.58108 | 2.10702 |
| user_001 | Jumping | 1.446047236475e12 | 7.58802 | -18.54643 | 3.10335 |
| user_001 | Jumping | 1.44604723654e12 | 0.767005 | -6.28393 | 0.689167 |
| user_001 | Jumping | 1.446047236604e12 | 0.307161 | 2.29982 | -0.498763 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 |
| user_001 | Jumping | 1.446047236734e12 | 2.2615 | -1.45557 | 2.14534 |
| user_001 | Jumping | 1.446047236798e12 | 1.80165 | 0.345482 | 0.765807 |
| user_001 | Jumping | 1.446047236863e12 | -0.650847 | -0.305964 | -1.15021 |
| user_001 | Jumping | 1.446047236928e12 | 3.48775 | -7.31858 | -0.728685 |
| user_001 | Jumping | 1.446047236992e12 | 5.32712 | -18.92964 | 0.114362 |
| user_001 | Jumping | 1.446047237057e12 | 10.34708 | -19.58108 | 2.37526 |
| user_001 | Jumping | 1.446047237122e12 | 5.97857 | -17.78002 | 0.344284 |
| user_001 | Jumping | 1.446047237187e12 | 2.8363 | -3.25663 | 0.152682 |
| user_001 | Jumping | 1.446047237252e12 | -0.995729 | 3.37279 | -1.64837 |
| user_001 | Jumping | 1.446047237317e12 | -1.64717 | -0.650847 | 1.41725 |
| user_001 | Jumping | 1.446047237381e12 | 1.30349 | -0.152682 | 1.45557 |
| user_001 | Jumping | 1.446047237446e12 | 2.79798 | 5.99e-4 | -0.460442 |
| user_001 | Jumping | 1.44604723751e12 | 1.41845 | -0.459245 | -1.38013 |
| user_001 | Jumping | 1.446047237575e12 | 3.90927 | -6.62882 | -1.49509 |
| user_001 | Jumping | 1.44604723764e12 | 4.98224 | -19.27452 | 1.57053 |
| user_001 | Jumping | 1.446047237705e12 | 12.22478 | -19.58108 | 2.0687 |
| user_001 | Jumping | 1.446047237769e12 | 4.5224 | -16.74538 | -0.383802 |
| user_001 | Jumping | 1.446047237834e12 | 0.690364 | -0.880768 | -0.537083 |
| user_001 | Jumping | 1.446047237899e12 | 1.26517 | 2.18486 | -1.03525 |
| user_001 | Jumping | 1.446047237964e12 | 2.6447 | -3.7722e-2 | 1.18733 |
| user_001 | Jumping | 1.446047238028e12 | 2.56806 | -0.114362 | 1.03405 |
| user_001 | Jumping | 1.446047238094e12 | -1.14901 | 0.11556 | -0.307161 |
| user_001 | Jumping | 1.446047238157e12 | -0.574206 | -0.114362 | -0.920286 |
| user_001 | Jumping | 1.446047238223e12 | 2.60638 | -3.40991 | -0.422122 |
| user_001 | Jumping | 1.446047238287e12 | 6.32345 | -17.62674 | -0.728685 |
| user_001 | Jumping | 1.446047238352e12 | 10.53868 | -19.58108 | 0.919089 |
| user_001 | Jumping | 1.446047238417e12 | 7.85626 | -18.20155 | 0.995729 |
| user_001 | Jumping | 1.446047238481e12 | -0.152682 | -1.41725 | -1.22685 |
| user_001 | Jumping | 1.446047238546e12 | 1.26517 | 2.22318 | -1.87829 |
| user_001 | Jumping | 1.446047238611e12 | 0.537083 | -0.497565 | 1.76214 |
| user_001 | Jumping | 1.446047238676e12 | 1.95493 | 0.230521 | 1.60885 |
| user_001 | Jumping | 1.446047238741e12 | 0.920286 | 0.537083 | -0.422122 |
| user_001 | Jumping | 1.446047238806e12 | 5.99e-4 | -0.650847 | 0.497565 |
| user_001 | Jumping | 1.44604723887e12 | 1.64837 | -1.99206 | -1.72501 |
| user_001 | Jumping | 1.446047238935e12 | 5.0972 | -14.75272 | -1.41845 |
| user_001 | Jumping | 1.446047239e12 | 10.50036 | -19.58108 | 1.91542 |
| user_001 | Jumping | 1.446047239064e12 | 8.16283 | -19.58108 | 1.95374 |
| user_001 | Jumping | 1.446047239129e12 | 3.56439 | -9.4262 | -1.22685 |
| user_001 | Jumping | 1.446047239194e12 | 0.920286 | 2.79798 | -1.34181 |
| user_001 | Jumping | 1.446047239259e12 | 0.1922 | 0.652044 | -0.537083 |
| user_001 | Jumping | 1.446047239324e12 | 1.64837 | -0.957409 | 1.41725 |
| user_001 | Jumping | 1.446047239388e12 | 1.99325 | -0.191003 | 0.957409 |
| user_001 | Jumping | 1.446047239453e12 | 1.26517 | -0.305964 | -5.99e-4 |
| user_001 | Jumping | 1.446047239518e12 | 2.4531 | -4.17632 | -1.34181 |
| user_001 | Jumping | 1.446047239583e12 | 4.44576 | -16.05561 | -0.537083 |
| user_001 | Jumping | 1.446047239647e12 | 9.58068 | -19.58108 | 1.53221 |
| user_001 | Jumping | 1.446047239712e12 | 8.04786 | -19.2362 | 2.0687 |
| user_001 | Jumping | 1.446047239777e12 | 1.80165 | -6.01569 | -0.422122 |
| user_001 | Jumping | 1.446047239841e12 | 0.11556 | 2.10822 | -2.6447 |
| user_001 | Jumping | 1.446047239906e12 | 0.345482 | -0.612526 | -1.22685 |
| user_001 | Jumping | 1.446047239971e12 | 1.45677 | -0.880768 | 2.2603 |
| user_001 | Jumping | 1.446047240036e12 | 0.843646 | 1.11189 | -0.537083 |
| user_001 | Jumping | 1.4460472401e12 | 0.996927 | 3.8919e-2 | -0.575403 |
| user_001 | Jumping | 1.446047240166e12 | 3.29615 | -3.98471 | -0.422122 |
| user_001 | Jumping | 1.446047240231e12 | 5.21216 | -16.7837 | -0.613724 |
| user_001 | Jumping | 1.446047240295e12 | 7.39642 | -19.58108 | 1.11069 |
| user_001 | Jumping | 1.44604724036e12 | 10.23212 | -19.58108 | 0.459245 |
| user_001 | Jumping | 1.446047240424e12 | 1.80165 | -12.14694 | -1.22685 |
| user_001 | Jumping | 1.446047240489e12 | 0.575403 | 1.83997 | 3.7722e-2 |
| user_001 | Jumping | 1.446047240553e12 | -0.152682 | 2.22318 | -3.0279 |
| user_001 | Jumping | 1.446047240618e12 | 0.268841 | -1.91542 | 3.06503 |
| user_001 | Jumping | 1.446047240683e12 | 1.72501 | -0.535886 | 1.83878 |
| user_001 | Jumping | 1.446047240748e12 | -1.18733 | 0.996927 | -1.26517 |
| user_001 | Jumping | 1.446047240813e12 | 0.920286 | -0.535886 | -1.91661 |
| user_001 | Jumping | 1.446047240878e12 | 4.40743 | -11.61046 | 0.459245 |
| user_001 | Jumping | 1.446047240941e12 | 6.78329 | -19.58108 | 4.82776 |
| user_001 | Jumping | 1.446047241007e12 | 9.35075 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047241072e12 | 5.71033 | -15.74905 | -1.15021 |
| user_001 | Jumping | 1.446047241136e12 | -1.57053 | -1.80046 | -0.15388 |
| user_001 | Jumping | 1.446047241201e12 | -0.267643 | 2.18486 | -2.14654 |
| user_001 | Jumping | 1.446047241266e12 | 2.22318 | -1.80046 | 1.80046 |
| user_001 | Jumping | 1.44604724133e12 | -1.34061 | 0.767005 | -2.03158 |
| user_001 | Jumping | 1.446047241395e12 | -0.382604 | -0.842448 | 0.382604 |
| user_001 | Jumping | 1.446047241459e12 | -0.765807 | -1.41725 | 7.6042e-2 |
| user_001 | Jumping | 1.446047241525e12 | 5.97857 | -10.34589 | -1.49509 |
| user_001 | Jumping | 1.446047241589e12 | 11.34341 | -19.58108 | 6.13065 |
| user_001 | Jumping | 1.446047241654e12 | 10.15548 | -19.58108 | 8.58315 |
| user_001 | Jumping | 1.446047241718e12 | 4.67568 | -11.61046 | -0.307161 |
| user_001 | Jumping | 1.446047241783e12 | -1.37893 | -0.650847 | 0.459245 |
| user_001 | Jumping | 1.446047241849e12 | -0.919089 | 2.6447 | -3.14286 |
| user_001 | Jumping | 1.446047241913e12 | 1.07357 | -1.03405 | -0.613724 |
| user_001 | Jumping | 1.446047241977e12 | 0.15388 | -0.919089 | 2.18366 |
| user_001 | Jumping | 1.446047242042e12 | -0.995729 | 0.460442 | 0.305964 |
| user_001 | Jumping | 1.446047242107e12 | 2.03158 | -0.382604 | 7.6042e-2 |
| user_001 | Jumping | 1.446047242172e12 | 6.93658 | -10.84405 | -0.422122 |
| user_001 | Jumping | 1.446047242237e12 | 13.79591 | -19.58108 | 1.49389 |
| user_001 | Jumping | 1.446047242302e12 | 15.86521 | -19.50444 | -1.72501 |
| user_001 | Jumping | 1.446047242366e12 | 4.79064 | -12.03198 | 1.8771 |
| user_001 | Jumping | 1.446047242431e12 | -0.497565 | 1.45677 | -4.10087 |
| user_001 | Jumping | 1.446047242495e12 | 0.11556 | 0.613724 | -1.30349 |
| user_001 | Jumping | 1.44604724256e12 | -0.459245 | -0.305964 | 2.56686 |
| user_001 | Jumping | 1.446047242625e12 | 2.29982 | 1.18853 | -0.767005 |
| user_001 | Jumping | 1.44604724269e12 | 1.38013 | -0.305964 | -1.87829 |
| user_001 | Jumping | 1.446047242754e12 | 2.52974 | -2.75846 | 0.152682 |
| user_001 | Jumping | 1.446047242819e12 | 5.32712 | -13.21991 | -1.34181 |
| user_001 | Jumping | 1.446047242884e12 | 10.50036 | -19.58108 | 1.8771 |
| user_001 | Jumping | 1.446047242948e12 | 13.02951 | -19.58108 | -0.498763 |
| user_001 | Jumping | 1.446047243013e12 | 4.33079 | -10.34589 | -2.4531 |
| user_001 | Jumping | 1.446047243078e12 | 0.613724 | 0.498763 | 2.49022 |
| user_001 | Jumping | 1.446047243143e12 | 0.307161 | 1.53341 | -2.49142 |
| user_001 | Jumping | 1.446047243207e12 | 7.7239e-2 | -0.344284 | 1.91542 |
| user_001 | Jumping | 1.446047243272e12 | 2.03158 | 0.1922 | -5.99e-4 |
| user_001 | Jumping | 1.446047243337e12 | 1.22685 | 0.230521 | -1.87829 |
| user_001 | Jumping | 1.446047243401e12 | 0.537083 | -1.26397 | 0.535886 |
| user_001 | Jumping | 1.446047243467e12 | 5.2888 | -11.22725 | 0.689167 |
| user_001 | Jumping | 1.446047243531e12 | 11.57333 | -19.58108 | 0.344284 |
| user_001 | Jumping | 1.446047243596e12 | 14.524 | -19.58108 | 2.33694 |
| user_001 | Jumping | 1.446047243661e12 | 3.18118 | -11.8787 | 0.191003 |
| user_001 | Jumping | 1.446047243725e12 | -1.07237 | 1.15021 | 0.152682 |
| user_001 | Jumping | 1.44604724379e12 | 0.422122 | 1.95493 | -1.87829 |
| user_001 | Jumping | 1.446047243855e12 | -0.535886 | -0.957409 | 1.18733 |
| user_001 | Jumping | 1.44604724392e12 | 3.60271 | -0.114362 | 0.574206 |
| user_001 | Jumping | 1.446047243984e12 | 2.87462 | -0.535886 | -0.268841 |
| user_001 | Jumping | 1.446047244049e12 | 2.2615 | -1.53221 | -0.11556 |
| user_001 | Jumping | 1.446047244114e12 | 6.85994 | -10.61413 | -1.11189 |
| user_001 | Jumping | 1.446047244178e12 | 7.97122 | -19.58108 | -7.7239e-2 |
| user_001 | Jumping | 1.446047244243e12 | 14.86888 | -19.58108 | -1.41845 |
| user_001 | Jumping | 1.446047244308e12 | 8.50771 | -14.48448 | -0.613724 |
| user_001 | Jumping | 1.446047244373e12 | -0.535886 | -1.18733 | 1.64717 |
| user_001 | Jumping | 1.446047244437e12 | -4.5212 | 3.37279 | 0.191003 |
| user_001 | Jumping | 1.446047244502e12 | -1.72382 | 0.690364 | 0.191003 |
| user_003 | Jumping | 1.446047778034e12 | -2.75846 | -7.28026 | -1.45677 |
| user_003 | Jumping | 1.446047778099e12 | -3.75479 | -7.08866 | -2.37646 |
| user_003 | Jumping | 1.446047778164e12 | -2.14534 | -6.74378 | -2.22318 |
| user_003 | Jumping | 1.446047778229e12 | -2.22198 | -6.01569 | -2.2615 |
| user_003 | Jumping | 1.446047778293e12 | -3.63983 | -9.04299 | -2.6447 |
| user_003 | Jumping | 1.446047778358e12 | -4.82776 | -17.09026 | -4.02423 |
| user_003 | Jumping | 1.446047778422e12 | -5.82409 | -19.46612 | -6.70665 |
| user_003 | Jumping | 1.446047778488e12 | 0.805325 | -10.92069 | -1.49509 |
| user_003 | Jumping | 1.446047778552e12 | -4.67448 | 1.22685 | 1.64717 |
| user_003 | Jumping | 1.446047778616e12 | -1.37893 | 0.460442 | -2.18486 |
| user_003 | Jumping | 1.446047778682e12 | 0.1922 | -0.650847 | -7.7239e-2 |
| user_003 | Jumping | 1.446047778746e12 | -0.842448 | 0.11556 | -1.26517 |
| user_003 | Jumping | 1.446047778811e12 | -1.26397 | -11.91702 | -3.64103 |
| user_003 | Jumping | 1.446047778876e12 | -1.41725 | -19.58108 | -0.767005 |
| user_003 | Jumping | 1.44604777894e12 | -1.76214 | -15.44249 | -2.68302 |
| user_003 | Jumping | 1.446047779006e12 | -0.574206 | -9.00468 | -1.76333 |
| user_003 | Jumping | 1.446047779071e12 | -3.14167 | -4.36792 | -1.68669 |
| user_003 | Jumping | 1.446047779135e12 | -3.37159 | -4.3296 | -2.4531 |
| user_003 | Jumping | 1.4460477792e12 | -4.06135 | -4.75112 | -3.10454 |
| user_003 | Jumping | 1.446047779265e12 | -4.36792 | -11.26557 | -0.345482 |
| user_003 | Jumping | 1.446047779329e12 | -5.97737 | -19.12124 | -4.25415 |
| user_003 | Jumping | 1.446047779394e12 | -3.75479 | -19.19788 | -4.9056 |
| user_003 | Jumping | 1.446047779459e12 | -0.459245 | -7.39522 | -0.537083 |
| user_003 | Jumping | 1.446047779524e12 | -2.91174 | 3.10454 | -2.37646 |
| user_003 | Jumping | 1.446047779589e12 | -1.41725 | -0.995729 | -0.230521 |
| user_003 | Jumping | 1.446047779653e12 | 0.422122 | -0.114362 | 0.344284 |
| user_003 | Jumping | 1.446047779718e12 | -0.574206 | -0.535886 | -1.18853 |
| user_003 | Jumping | 1.446047779783e12 | -1.72382 | -13.98632 | -4.10087 |
| user_003 | Jumping | 1.446047779847e12 | -3.14167 | -19.58108 | -3.48775 |
| user_003 | Jumping | 1.446047779912e12 | -2.68182 | -13.21991 | -3.18118 |
| user_003 | Jumping | 1.446047779977e12 | -0.267643 | -8.92803 | -2.10822 |
| user_003 | Jumping | 1.446047780042e12 | -3.17999 | -3.98471 | -0.728685 |
| user_003 | Jumping | 1.446047780107e12 | -2.91174 | -4.48288 | -2.18486 |
| user_003 | Jumping | 1.446047780171e12 | -3.37159 | -5.78577 | -3.87095 |
| user_003 | Jumping | 1.446047780236e12 | -4.02303 | -9.84772 | -0.383802 |
| user_003 | Jumping | 1.446047780301e12 | -6.47553 | -17.81835 | -4.13919 |
| user_003 | Jumping | 1.446047780365e12 | -3.86975 | -19.58108 | -5.40376 |
| user_003 | Jumping | 1.44604778043e12 | -1.99206 | -14.44616 | -3.64103 |
| user_003 | Jumping | 1.446047780495e12 | -2.29862 | -1.18733 | -0.307161 |
| user_003 | Jumping | 1.446047780559e12 | -1.8771 | 1.99325 | -1.68669 |
| user_003 | Jumping | 1.446047780625e12 | 5.99e-4 | -0.344284 | 0.650847 |
| user_003 | Jumping | 1.446047780689e12 | -0.459245 | -0.191003 | -0.613724 |
| user_003 | Jumping | 1.446047780754e12 | -2.60518 | -6.9737 | -4.714 |
| user_003 | Jumping | 1.446047780818e12 | -1.41725 | -19.58108 | -2.56806 |
| user_003 | Jumping | 1.446047780884e12 | -3.40991 | -17.51178 | -4.59904 |
| user_003 | Jumping | 1.446047780948e12 | -1.95374 | -10.61413 | -2.91294 |
| user_003 | Jumping | 1.446047781013e12 | -3.06503 | -6.70546 | -1.76333 |
| user_003 | Jumping | 1.446047781078e12 | -2.6435 | -7.20362 | -1.87829 |
| user_003 | Jumping | 1.446047781143e12 | -2.60518 | -7.28026 | -1.64837 |
| user_003 | Jumping | 1.446047781208e12 | -1.72382 | -6.51385 | -2.0699 |
| user_003 | Jumping | 1.446047781272e12 | -0.689167 | -5.55585 | -2.37646 |
| user_003 | Jumping | 1.446047781337e12 | -3.67815 | -8.00835 | -1.99325 |
| user_003 | Jumping | 1.446047781402e12 | -5.63249 | -13.64143 | -3.44943 |
| user_003 | Jumping | 1.446047781467e12 | -5.97737 | -18.16323 | -5.59536 |
| user_003 | Jumping | 1.446047781531e12 | -2.03038 | -19.2362 | -5.25048 |
| user_003 | Jumping | 1.446047781596e12 | -2.14534 | -8.88971 | -1.18853 |
| user_003 | Jumping | 1.44604778166e12 | -3.06503 | 2.52974 | -0.767005 |
| user_003 | Jumping | 1.446047781725e12 | 0.230521 | 0.268841 | -0.345482 |
| user_003 | Jumping | 1.44604778179e12 | 0.15388 | -0.535886 | -0.690364 |
| user_003 | Jumping | 1.446047781855e12 | -0.420925 | -2.18366 | -2.68302 |
| user_003 | Jumping | 1.446047781919e12 | -3.63983 | -17.47346 | -4.44576 |
| user_003 | Jumping | 1.446047781985e12 | -2.91174 | -18.35483 | -4.714 |
| user_003 | Jumping | 1.446047782049e12 | -0.919089 | -10.88237 | -2.2615 |
| user_003 | Jumping | 1.446047782114e12 | -3.02671 | -6.93538 | -2.68302 |
| user_003 | Jumping | 1.446047782178e12 | -2.75846 | -7.3569 | -2.10822 |
| user_003 | Jumping | 1.446047782244e12 | -1.45557 | -7.85507 | -1.22685 |
| user_003 | Jumping | 1.446047782309e12 | -1.8771 | -6.32225 | -1.99325 |
| user_003 | Jumping | 1.446047782373e12 | -1.64717 | -5.90073 | -2.33814 |
| user_003 | Jumping | 1.446047782438e12 | -2.49022 | -8.08499 | -1.80165 |
| user_003 | Jumping | 1.446047782503e12 | -4.40624 | -11.26557 | -2.37646 |
| user_003 | Jumping | 1.446047782568e12 | -5.13432 | -17.1669 | -4.67568 |
| user_003 | Jumping | 1.446047782632e12 | -3.40991 | -19.54276 | -5.71033 |
| user_003 | Jumping | 1.446047782697e12 | -1.99206 | -13.48815 | -4.06255 |
| user_003 | Jumping | 1.446047782762e12 | -2.22198 | -1.68549 | 0.267643 |
| user_003 | Jumping | 1.446047782827e12 | -1.91542 | 1.80165 | -1.68669 |
| user_003 | Jumping | 1.446047782891e12 | 0.575403 | -0.152682 | 0.459245 |
| user_003 | Jumping | 1.446047782956e12 | -0.842448 | -0.804128 | -1.03525 |
| user_003 | Jumping | 1.446047783021e12 | -1.76214 | -12.22358 | -5.67201 |
| user_003 | Jumping | 1.446047783086e12 | -2.33694 | -19.19788 | -4.02423 |
| user_003 | Jumping | 1.446047783151e12 | -1.14901 | -14.98264 | -4.13919 |
| user_003 | Jumping | 1.446047783215e12 | -2.22198 | -9.04299 | -2.91294 |
| user_003 | Jumping | 1.44604778328e12 | -2.41358 | -5.59417 | -0.613724 |
| user_003 | Jumping | 1.446047783344e12 | -2.60518 | -5.78577 | -2.33814 |
| user_003 | Jumping | 1.446047783409e12 | -3.67815 | -4.44456 | -2.4531 |
| user_003 | Jumping | 1.446047783474e12 | -2.8351 | -4.63616 | -3.75599 |
| user_003 | Jumping | 1.446047783539e12 | -1.64717 | -16.4005 | -0.843646 |
| user_003 | Jumping | 1.446047783604e12 | -6.62882 | -19.58108 | -8.58435 |
| user_003 | Jumping | 1.446047783669e12 | -1.72382 | -14.67608 | -3.52607 |
| user_003 | Jumping | 1.446047783733e12 | -1.57053 | 1.34181 | 0.612526 |
| user_003 | Jumping | 1.446047783798e12 | -1.18733 | 1.22685 | -1.38013 |
| user_003 | Jumping | 1.446047783863e12 | -0.612526 | -0.612526 | -0.268841 |
| user_003 | Jumping | 1.446047783928e12 | -1.22565 | 0.345482 | -0.805325 |
| user_003 | Jumping | 1.446047783992e12 | -1.11069 | -2.2603 | -2.56806 |
| user_003 | Jumping | 1.446047784057e12 | -2.8351 | -17.62674 | -5.02056 |
| user_003 | Jumping | 1.446047784122e12 | -3.90807 | -19.19788 | -5.59536 |
| user_003 | Jumping | 1.446047784187e12 | -1.72382 | -12.6451 | -3.64103 |
| user_003 | Jumping | 1.446047784252e12 | -1.49389 | -6.7821 | -2.0699 |
| user_003 | Jumping | 1.446047784316e12 | -2.14534 | -4.67448 | -1.15021 |
| user_003 | Jumping | 1.446047784381e12 | -2.91174 | -6.93538 | -2.6447 |
| user_003 | Jumping | 1.446047784446e12 | -3.71647 | -5.86241 | -2.56806 |
| user_003 | Jumping | 1.44604778451e12 | -0.344284 | -6.51385 | -2.8363 |
| user_003 | Jumping | 1.446047784575e12 | -4.02303 | -14.98264 | -2.2615 |
| user_003 | Jumping | 1.446047784639e12 | -5.36425 | -19.58108 | -8.89091 |
| user_003 | Jumping | 1.446047784704e12 | -0.191003 | -14.906 | -3.94759 |
| user_003 | Jumping | 1.446047784769e12 | -2.4519 | 7.7239e-2 | 1.11069 |
| user_003 | Jumping | 1.446047784834e12 | -2.4519 | 1.22685 | -1.41845 |
| user_003 | Jumping | 1.446047784899e12 | -0.344284 | 0.345482 | -0.15388 |
| user_003 | Jumping | 1.446047784964e12 | -0.842448 | 0.422122 | -0.996927 |
| user_003 | Jumping | 1.446047785029e12 | -2.29862 | -4.3296 | -4.63736 |
| user_003 | Jumping | 1.446047785093e12 | -0.765807 | -18.92964 | -3.98591 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 |
| user_003 | Jumping | 1.446047785222e12 | -1.11069 | -12.6451 | -1.22685 |
| user_003 | Jumping | 1.446047785287e12 | -3.44823 | -7.24194 | -2.18486 |
| user_003 | Jumping | 1.446047785352e12 | -2.49022 | -6.51385 | -2.95126 |
| user_003 | Jumping | 1.446047785417e12 | -2.10702 | -6.85874 | -1.22685 |
| user_003 | Jumping | 1.446047785482e12 | -2.79678 | -7.05034 | -2.14654 |
| user_003 | Jumping | 1.446047785546e12 | -2.56686 | -5.01936 | -2.52974 |
| user_003 | Jumping | 1.446047785611e12 | -2.87342 | -6.62882 | -2.56806 |
| user_003 | Jumping | 1.446047785676e12 | -4.63616 | -14.44616 | -2.52974 |
| user_003 | Jumping | 1.446047785741e12 | -5.67081 | -19.58108 | -6.59169 |
| user_003 | Jumping | 1.446047785805e12 | -3.67815 | -17.66507 | -6.63001 |
| user_003 | Jumping | 1.44604778587e12 | -0.919089 | -4.82776 | -0.268841 |
| user_003 | Jumping | 1.446047785934e12 | -2.91174 | 2.87462 | -2.33814 |
| user_003 | Jumping | 1.446047785999e12 | -0.420925 | -0.420925 | 0.919089 |
| user_003 | Jumping | 1.446047786065e12 | -3.7722e-2 | 0.230521 | 0.382604 |
| user_003 | Jumping | 1.446047786129e12 | -0.842448 | -0.497565 | -3.0279 |
| user_003 | Jumping | 1.446047786194e12 | -0.995729 | -15.48081 | -4.33079 |
| user_003 | Jumping | 1.446047786259e12 | -3.98471 | -19.50444 | -6.89825 |
| user_003 | Jumping | 1.446047786324e12 | -0.574206 | -13.29655 | -3.94759 |
| user_003 | Jumping | 1.446047786388e12 | -1.41725 | -8.85139 | -1.38013 |
| user_003 | Jumping | 1.446047786453e12 | -3.94639 | -5.59417 | -3.14286 |
| user_003 | Jumping | 1.446047786518e12 | -2.18366 | -8.04667 | -3.06622 |
| user_003 | Jumping | 1.446047786582e12 | -1.60885 | -8.92803 | -1.15021 |
| user_003 | Jumping | 1.446047786647e12 | -2.72014 | -5.13432 | -2.33814 |
| user_003 | Jumping | 1.446047786712e12 | -2.03038 | -4.25296 | -3.18118 |
| user_003 | Jumping | 1.446047786777e12 | -3.48655 | -9.84772 | -1.45677 |
| user_003 | Jumping | 1.446047786841e12 | -6.01569 | -18.96796 | -6.78329 |
| user_003 | Jumping | 1.446047786906e12 | -4.44456 | -19.4278 | -8.62267 |
| user_003 | Jumping | 1.44604778697e12 | -0.267643 | -8.92803 | -1.57173 |
| user_003 | Jumping | 1.446047787035e12 | -2.91174 | 3.41111 | -1.53341 |
| user_003 | Jumping | 1.4460477871e12 | 0.268841 | -0.765807 | 0.689167 |
| user_003 | Jumping | 1.446047787165e12 | -0.650847 | 0.383802 | -0.728685 |
| user_003 | Jumping | 1.44604778723e12 | -0.880768 | 0.422122 | -1.03525 |
| user_003 | Jumping | 1.446047787294e12 | -1.60885 | -11.11229 | -5.4804 |
| user_003 | Jumping | 1.446047787358e12 | -4.59784 | -19.35116 | -5.94025 |
| user_003 | Jumping | 1.446047787424e12 | -2.41358 | -15.71073 | -5.74865 |
| user_003 | Jumping | 1.446047787489e12 | -1.26397 | -9.34956 | -3.06622 |
| user_003 | Jumping | 1.446047787553e12 | -3.44823 | -6.47553 | -2.75966 |
| user_003 | Jumping | 1.446047787618e12 | -2.4519 | -7.97003 | -2.2615 |
| user_003 | Jumping | 1.446047787683e12 | -1.95374 | -7.81675 | -1.83997 |
| user_003 | Jumping | 1.446047787747e12 | -2.75846 | -4.67448 | -3.06622 |
| user_003 | Jumping | 1.446047787812e12 | -2.22198 | -5.24928 | -2.0699 |
| user_003 | Jumping | 1.446047787877e12 | -3.29495 | -12.41518 | -1.49509 |
| user_003 | Jumping | 1.446047787942e12 | -5.44089 | -19.46612 | -6.20849 |
| user_003 | Jumping | 1.446047788007e12 | -3.79311 | -18.69972 | -7.70298 |
| user_003 | Jumping | 1.446047788071e12 | -0.382604 | -7.1653 | -1.83997 |
| user_003 | Jumping | 1.446047788136e12 | -3.14167 | 2.72134 | -0.575403 |
| user_003 | Jumping | 1.446047788201e12 | 0.460442 | -0.152682 | 0.114362 |
| user_003 | Jumping | 1.446047788265e12 | -0.842448 | 0.652044 | -0.230521 |
| user_003 | Jumping | 1.446047788331e12 | -2.2603 | -0.229323 | -1.45677 |
| user_003 | Jumping | 1.446047788395e12 | -1.45557 | -13.71807 | -6.63001 |
| user_003 | Jumping | 1.44604778846e12 | -3.14167 | -19.54276 | -6.32345 |
| user_003 | Jumping | 1.446047788524e12 | -1.83878 | -13.06663 | -4.40743 |
| user_003 | Jumping | 1.44604778859e12 | -1.83878 | -9.77108 | -3.25783 |
| user_003 | Jumping | 1.446047788654e12 | -3.14167 | -6.55217 | -2.6447 |
| user_003 | Jumping | 1.446047788719e12 | -2.10702 | -7.31858 | -2.10822 |
| user_003 | Jumping | 1.446047788784e12 | -2.14534 | -5.90073 | -2.0699 |
| user_003 | Jumping | 1.446047788848e12 | -1.57053 | -4.9044 | -2.75966 |
| user_003 | Jumping | 1.446047788913e12 | -2.75846 | -7.5485 | -1.76333 |
| user_003 | Jumping | 1.446047788978e12 | -4.98104 | -15.97897 | -4.67568 |
| user_003 | Jumping | 1.446047789043e12 | -3.10335 | -19.58108 | -8.16283 |
| user_003 | Jumping | 1.446047789107e12 | -1.68549 | -16.36218 | -5.25048 |
| user_003 | Jumping | 1.446047789172e12 | -2.52854 | -2.0687 | -0.345482 |
| user_003 | Jumping | 1.446047789236e12 | -1.72382 | 2.41478 | -2.37646 |
| user_003 | Jumping | 1.446047789301e12 | 0.843646 | -0.382604 | 1.11069 |
| user_003 | Jumping | 1.446047789366e12 | -0.689167 | 0.230521 | -0.728685 |
| user_003 | Jumping | 1.446047789431e12 | -1.14901 | -1.95374 | -2.72134 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 |
| user_003 | Jumping | 1.44604778956e12 | -3.29495 | -18.96796 | -5.05888 |
| user_003 | Jumping | 1.446047789625e12 | -2.22198 | -12.0703 | -3.98591 |
| user_003 | Jumping | 1.44604778969e12 | -2.79678 | -7.7401 | -2.37646 |
| user_003 | Jumping | 1.446047789755e12 | -1.95374 | -5.74745 | -2.22318 |
| user_003 | Jumping | 1.446047789818e12 | -2.03038 | -6.85874 | -1.68669 |
| user_003 | Jumping | 1.446047789883e12 | -2.91174 | -5.67081 | -2.52974 |
| user_003 | Jumping | 1.446047789949e12 | -1.49389 | -4.94272 | -2.75966 |
| user_003 | Jumping | 1.446047790013e12 | -3.37159 | -10.88237 | -1.57173 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 |
| user_003 | Jumping | 1.446047790143e12 | -4.48288 | -17.5501 | -6.9749 |
| user_003 | Jumping | 1.446047790207e12 | -0.612526 | -2.68182 | -0.613724 |
| user_003 | Jumping | 1.446047790272e12 | -1.8771 | 2.68302 | -2.03158 |
| user_003 | Jumping | 1.446047790337e12 | 1.15021 | -0.919089 | 1.64717 |
| user_003 | Jumping | 1.446047790402e12 | -2.03038 | 0.307161 | -1.22685 |
| user_003 | Jumping | 1.446047790467e12 | -1.34061 | -0.191003 | -1.61005 |
| user_003 | Jumping | 1.446047790531e12 | -3.02671 | -13.64143 | -5.17384 |
| user_003 | Jumping | 1.446047790596e12 | -4.75112 | -19.58108 | -7.62634 |
| user_003 | Jumping | 1.44604779066e12 | -2.60518 | -15.05928 | -6.01689 |
| user_003 | Jumping | 1.446047790726e12 | -1.26397 | -8.12331 | -3.10454 |
| user_003 | Jumping | 1.44604779079e12 | -3.25663 | -5.97737 | -3.0279 |
| user_003 | Jumping | 1.446047790855e12 | -0.420925 | -6.24561 | -2.2615 |
| user_003 | Jumping | 1.446047790919e12 | -1.83878 | -3.40991 | -3.60271 |
| user_003 | Jumping | 1.446047790984e12 | -3.94639 | -5.36425 | -3.75599 |
| user_003 | Jumping | 1.446047791049e12 | -2.2603 | -13.98632 | -3.67935 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 |
| user_003 | Jumping | 1.446047791179e12 | -2.52854 | -15.36585 | -6.66833 |
| user_003 | Jumping | 1.446047791243e12 | -0.574206 | -0.497565 | -0.230521 |
| user_003 | Jumping | 1.446047791308e12 | -2.0687 | 0.958607 | -0.575403 |
| user_003 | Jumping | 1.446047791373e12 | 0.11556 | -0.497565 | 0.344284 |
| user_003 | Jumping | 1.446047791438e12 | -1.30229 | 1.34181 | -1.11189 |
| user_003 | Jumping | 1.446047791503e12 | -0.382604 | -0.880768 | -2.22318 |
| user_003 | Jumping | 1.446047791568e12 | -2.87342 | -15.97897 | -6.28513 |
| user_003 | Jumping | 1.446047791632e12 | -2.95007 | -19.38948 | -7.51138 |
| user_003 | Jumping | 1.446047791697e12 | -4.59784 | -13.37319 | -5.02056 |
| user_003 | Jumping | 1.446047791762e12 | -2.14534 | -9.84772 | -3.2195 |
| user_003 | Jumping | 1.446047791827e12 | -2.29862 | -6.55217 | -2.49142 |
| user_003 | Jumping | 1.446047791891e12 | -1.68549 | -7.47186 | -2.29982 |
| user_003 | Jumping | 1.446047791956e12 | -1.22565 | -8.16163 | -2.56806 |
| user_003 | Jumping | 1.446047792021e12 | -2.95007 | -5.096 | -2.52974 |
| user_003 | Jumping | 1.446047792085e12 | -4.02303 | -4.06135 | -7.7413 |
| user_003 | Jumping | 1.446047792151e12 | -2.60518 | -14.40784 | 0.727487 |
| user_003 | Jumping | 1.446047792215e12 | -6.24561 | -19.58108 | -8.89091 |
| user_003 | Jumping | 1.44604779228e12 | -1.60885 | -12.56846 | -3.2195 |
| user_003 | Jumping | 1.446047792344e12 | -3.10335 | 3.14286 | -1.26517 |
| user_003 | Jumping | 1.44604779241e12 | -3.7722e-2 | 5.99e-4 | -0.11556 |
| user_003 | Jumping | 1.446047792474e12 | 0.268841 | -0.650847 | 0.114362 |
| user_003 | Jumping | 1.446047792539e12 | -2.68182 | 0.996927 | -1.99325 |
| user_003 | Jumping | 1.446047792603e12 | -1.64717 | -4.67448 | -5.51872 |
| user_003 | Jumping | 1.446047792669e12 | -2.87342 | -18.96796 | -5.86361 |
| user_003 | Jumping | 1.446047792733e12 | -3.29495 | -18.66139 | -4.44576 |
| user_003 | Jumping | 1.446047792798e12 | -4.02303 | -13.21991 | -4.79064 |
| user_003 | Jumping | 1.446047792863e12 | -2.95007 | -7.97003 | -2.79798 |
| user_003 | Jumping | 1.446047792928e12 | -3.17999 | -6.20729 | -2.29982 |
| user_003 | Jumping | 1.446047792992e12 | -3.25663 | -5.40257 | -2.37646 |
| user_003 | Jumping | 1.446047793057e12 | -3.40991 | -4.5212 | -3.18118 |
| user_003 | Jumping | 1.446047793122e12 | -3.29495 | -3.90807 | -3.94759 |
| user_003 | Jumping | 1.446047793186e12 | -4.7128 | -14.7144 | -2.75966 |
| user_003 | Jumping | 1.446047793251e12 | -4.55952 | -19.58108 | -7.77962 |
| user_003 | Jumping | 1.446047793316e12 | -2.95007 | -16.32385 | -6.93658 |
| user_003 | Jumping | 1.44604779338e12 | -1.26397 | -2.14534 | -1.38013 |
| user_003 | Jumping | 1.446047793445e12 | -2.10702 | 2.33814 | -1.64837 |
| user_003 | Jumping | 1.44604779351e12 | -0.420925 | -0.267643 | 1.07237 |
| user_003 | Jumping | 1.446047793575e12 | -0.995729 | 0.383802 | -0.537083 |
| user_003 | Jumping | 1.44604779364e12 | -1.14901 | -1.76214 | -2.29982 |
| user_003 | Jumping | 1.446047793704e12 | -2.56686 | -14.82936 | -6.28513 |
| user_003 | Jumping | 1.446047793768e12 | -3.40991 | -19.31284 | -7.3581 |
| user_003 | Jumping | 1.446047793834e12 | -3.52487 | -13.87135 | -5.63368 |
| user_003 | Jumping | 1.446047793899e12 | -2.68182 | -10.84405 | -3.10454 |
| user_003 | Jumping | 1.446047793963e12 | -2.72014 | -4.78944 | -2.72134 |
| user_003 | Jumping | 1.446047794028e12 | -1.03405 | -4.94272 | -1.68669 |
| user_003 | Jumping | 1.446047794093e12 | -2.10702 | -5.93905 | -2.14654 |
| user_003 | Jumping | 1.446047794158e12 | -1.57053 | -5.90073 | -3.2195 |
| user_003 | Jumping | 1.446047794222e12 | -4.3296 | -8.08499 | -1.15021 |
| user_003 | Jumping | 1.446047794287e12 | -4.48288 | -19.08292 | -6.36177 |
| user_003 | Jumping | 1.446047794352e12 | -4.67448 | -19.58108 | -8.27779 |
| user_003 | Jumping | 1.446047794416e12 | 5.99e-4 | -10.76741 | -2.33814 |
| user_003 | Jumping | 1.446047794481e12 | -1.95374 | 3.41111 | -1.07357 |
| user_003 | Jumping | 1.446047794546e12 | -0.152682 | -0.689167 | 0.229323 |
| user_003 | Jumping | 1.446047794611e12 | -0.957409 | 0.422122 | -0.460442 |
| user_003 | Jumping | 1.446047794676e12 | -0.957409 | 0.996927 | -0.881966 |
| user_003 | Jumping | 1.44604779474e12 | -1.57053 | -3.60151 | -4.75232 |
| user_003 | Jumping | 1.446047794805e12 | -1.99206 | -18.35483 | -5.78697 |
| user_003 | Jumping | 1.44604779487e12 | -3.71647 | -19.2362 | -6.47673 |
| user_003 | Jumping | 1.446047794935e12 | -0.842448 | -14.94432 | -4.17751 |
| user_002 | Standing | 1.446309439342e12 | -2.49022 | -9.19628 | -1.11189 |
| user_002 | Standing | 1.446309439349e12 | -2.41358 | -9.15796 | -1.22685 |
| user_002 | Standing | 1.446309439358e12 | -2.56686 | -9.15796 | -1.30349 |
| user_002 | Standing | 1.446309439365e12 | -2.75846 | -9.08132 | -1.11189 |
| user_002 | Standing | 1.446309439373e12 | -2.75846 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309439382e12 | -2.8351 | -9.15796 | -0.920286 |
| user_002 | Standing | 1.44630943939e12 | -2.8357 | -9.15855 | -0.919687 |
| user_002 | Standing | 1.446309439398e12 | -2.95007 | -9.2346 | -0.881966 |
| user_002 | Standing | 1.446309439406e12 | -2.8351 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439414e12 | -2.8351 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439422e12 | -2.95007 | -9.08132 | -0.920286 |
| user_002 | Standing | 1.44630943943e12 | -2.95007 | -8.88971 | -0.996927 |
| user_002 | Standing | 1.446309439438e12 | -2.91174 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439447e12 | -3.02671 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309439454e12 | -2.79678 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439463e12 | -2.87342 | -9.08132 | -1.34181 |
| user_002 | Standing | 1.446309439471e12 | -2.98839 | -9.19628 | -1.22685 |
| user_002 | Standing | 1.446309439479e12 | -2.95007 | -9.00468 | -1.30349 |
| user_002 | Standing | 1.446309439487e12 | -2.98839 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439495e12 | -3.06503 | -9.00468 | -0.958607 |
| user_002 | Standing | 1.446309439503e12 | -3.02671 | -9.04299 | -0.881966 |
| user_002 | Standing | 1.446309439511e12 | -2.91174 | -9.00468 | -0.920286 |
| user_002 | Standing | 1.446309439519e12 | -2.95007 | -9.08132 | -0.767005 |
| user_002 | Standing | 1.446309439527e12 | -2.8351 | -9.00468 | -0.958607 |
| user_002 | Standing | 1.446309439536e12 | -2.95007 | -9.00468 | -0.996927 |
| user_002 | Standing | 1.446309439543e12 | -2.75846 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309439552e12 | -2.6435 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.44630943956e12 | -2.72014 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439568e12 | -2.68182 | -9.04299 | -1.11189 |
| user_002 | Standing | 1.446309439576e12 | -2.60518 | -9.00468 | -1.11189 |
| user_002 | Standing | 1.446309439584e12 | -2.52854 | -8.88971 | -1.15021 |
| user_002 | Standing | 1.446309439593e12 | -2.6435 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309439601e12 | -2.60518 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309439609e12 | -2.6435 | -9.04299 | -0.996927 |
| user_002 | Standing | 1.446309439616e12 | -2.75846 | -9.04299 | -0.920286 |
| user_002 | Standing | 1.446309439624e12 | -2.6435 | -9.19628 | -0.805325 |
| user_002 | Standing | 1.446309439633e12 | -2.79678 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309439641e12 | -2.87342 | -8.92803 | -0.958607 |
| user_002 | Standing | 1.446309439649e12 | -2.60518 | -9.11964 | -0.843646 |
| user_002 | Standing | 1.446309439657e12 | -2.8351 | -9.08132 | -0.843646 |
| user_002 | Standing | 1.446309439665e12 | -2.8351 | -9.2346 | -0.920286 |
| user_002 | Standing | 1.446309439674e12 | -2.72014 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439682e12 | -2.87342 | -9.15796 | -1.18853 |
| user_002 | Standing | 1.44630943969e12 | -3.06503 | -9.2346 | -1.18853 |
| user_002 | Standing | 1.446309439698e12 | -2.8351 | -9.08132 | -0.996927 |
| user_002 | Standing | 1.446309439705e12 | -2.98839 | -9.04299 | -1.03525 |
| user_002 | Standing | 1.446309439713e12 | -3.17999 | -8.96635 | -1.18853 |
| user_002 | Standing | 1.446309439722e12 | -3.21831 | -8.88971 | -1.18853 |
| user_002 | Standing | 1.446309439729e12 | -3.17999 | -8.92803 | -1.15021 |
| user_002 | Standing | 1.446309439738e12 | -3.17999 | -8.77475 | -1.22685 |
| user_002 | Standing | 1.446309439746e12 | -3.33327 | -8.65979 | -1.07357 |
| user_002 | Standing | 1.446309439754e12 | -3.37159 | -8.58315 | -1.34181 |
| user_002 | Standing | 1.446309439762e12 | -3.33327 | -8.77475 | -1.18853 |
| user_002 | Standing | 1.446309439771e12 | -3.33327 | -8.58315 | -1.07357 |
| user_002 | Standing | 1.446309439779e12 | -3.14167 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309439787e12 | -3.25663 | -8.85139 | -0.881966 |
| user_002 | Standing | 1.446309439795e12 | -3.10335 | -8.85139 | -0.920286 |
| user_002 | Standing | 1.446309439803e12 | -2.87342 | -8.85139 | -0.843646 |
| user_002 | Standing | 1.446309439811e12 | -2.91174 | -8.92803 | -0.767005 |
| user_002 | Standing | 1.446309439819e12 | -2.87342 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309439827e12 | -2.98839 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309439835e12 | -2.79678 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309439843e12 | -2.87342 | -9.08132 | -0.920286 |
| user_002 | Standing | 1.446309439851e12 | -2.95007 | -9.15796 | -0.805325 |
| user_002 | Standing | 1.446309439859e12 | -3.02671 | -8.85139 | -0.728685 |
| user_002 | Standing | 1.446309439868e12 | -2.98839 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309439875e12 | -2.98839 | -8.96635 | -0.690364 |
| user_002 | Standing | 1.446309439883e12 | -2.91174 | -8.96635 | -0.881966 |
| user_002 | Standing | 1.446309439892e12 | -3.02671 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.4463094399e12 | -3.06503 | -9.04299 | -0.767005 |
| user_002 | Standing | 1.446309439908e12 | -2.87342 | -8.73643 | -0.652044 |
| user_002 | Standing | 1.446309439916e12 | -2.95007 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309439924e12 | -3.10335 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309439932e12 | -2.91174 | -8.77475 | -0.652044 |
| user_002 | Standing | 1.44630943994e12 | -2.91174 | -9.04299 | -0.805325 |
| user_002 | Standing | 1.446309439948e12 | -2.91174 | -9.04299 | -0.767005 |
| user_002 | Standing | 1.446309439957e12 | -3.02671 | -9.04299 | -0.652044 |
| user_002 | Standing | 1.446309439965e12 | -3.06503 | -9.27292 | -0.805325 |
| user_002 | Standing | 1.446309439973e12 | -3.14167 | -9.00468 | -0.767005 |
| user_002 | Standing | 1.44630943998e12 | -2.95007 | -8.96635 | -0.843646 |
| user_002 | Standing | 1.446309439989e12 | -3.10335 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439997e12 | -2.98839 | -9.19628 | -1.03525 |
| user_002 | Standing | 1.446309440004e12 | -3.10335 | -9.19628 | -1.15021 |
| user_002 | Standing | 1.446309440013e12 | -2.91174 | -9.11964 | -1.18853 |
| user_002 | Standing | 1.446309440021e12 | -2.91174 | -9.08132 | -1.22685 |
| user_002 | Standing | 1.446309440029e12 | -2.95007 | -9.00468 | -1.26517 |
| user_002 | Standing | 1.446309440038e12 | -3.06503 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309440045e12 | -3.06503 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440054e12 | -3.10335 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309440062e12 | -3.10335 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309440069e12 | -3.10335 | -8.96635 | -1.03525 |
| user_002 | Standing | 1.446309440078e12 | -3.17999 | -9.00468 | -1.11189 |
| user_002 | Standing | 1.446309440086e12 | -3.14167 | -8.85139 | -0.996927 |
| user_002 | Standing | 1.446309440094e12 | -3.17999 | -9.00468 | -0.728685 |
| user_002 | Standing | 1.446309440102e12 | -3.17999 | -8.96635 | -0.958607 |
| user_002 | Standing | 1.446309440111e12 | -3.10335 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440119e12 | -3.17999 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440126e12 | -3.02671 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440135e12 | -3.06503 | -9.00468 | -0.728685 |
| user_002 | Standing | 1.446309440142e12 | -3.21831 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309440151e12 | -3.33327 | -8.85139 | -0.767005 |
| user_002 | Standing | 1.446309440158e12 | -3.21831 | -9.04299 | -0.652044 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440175e12 | -3.37159 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309440183e12 | -3.37159 | -9.00468 | -0.652044 |
| user_002 | Standing | 1.446309440192e12 | -3.33327 | -8.81307 | -0.460442 |
| user_002 | Standing | 1.446309440199e12 | -3.21831 | -9.08132 | -0.537083 |
| user_002 | Standing | 1.446309440208e12 | -3.21831 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309440215e12 | -3.14167 | -8.96635 | -0.575403 |
| user_002 | Standing | 1.446309440224e12 | -3.17999 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309440232e12 | -3.17999 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309440239e12 | -3.14167 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309440248e12 | -3.10335 | -9.04299 | -0.843646 |
| user_002 | Standing | 1.446309440255e12 | -3.14167 | -8.88971 | -0.767005 |
| user_002 | Standing | 1.446309440264e12 | -3.06503 | -9.11964 | -0.805325 |
| user_002 | Standing | 1.446309440272e12 | -3.14167 | -8.96635 | -0.690364 |
| user_002 | Standing | 1.44630944028e12 | -3.29495 | -9.08132 | -0.843646 |
| user_002 | Standing | 1.446309440288e12 | -3.33327 | -8.81307 | -0.575403 |
| user_002 | Standing | 1.446309440296e12 | -3.21831 | -8.88971 | -0.575403 |
| user_002 | Standing | 1.446309440305e12 | -3.21831 | -8.96635 | -0.613724 |
| user_002 | Standing | 1.446309440312e12 | -3.14167 | -8.77475 | -0.537083 |
| user_002 | Standing | 1.44630944032e12 | -3.10335 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440329e12 | -3.21831 | -9.00468 | -0.613724 |
| user_002 | Standing | 1.446309440337e12 | -3.14167 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440345e12 | -3.21831 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309440353e12 | -3.06503 | -8.92803 | -0.575403 |
| user_002 | Standing | 1.446309440361e12 | -3.25663 | -9.11964 | -0.728685 |
| user_002 | Standing | 1.446309440369e12 | -3.21831 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.446309440377e12 | -3.06503 | -8.92803 | -0.843646 |
| user_002 | Standing | 1.446309440385e12 | -3.06503 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309440394e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440402e12 | -3.02671 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.44630944041e12 | -3.21831 | -8.96635 | -0.652044 |
| user_002 | Standing | 1.446309440417e12 | -3.25663 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309440426e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440434e12 | -3.37159 | -8.85139 | -0.767005 |
| user_002 | Standing | 1.446309440443e12 | -3.21831 | -8.85139 | -0.537083 |
| user_002 | Standing | 1.44630944045e12 | -3.10335 | -9.11964 | -0.767005 |
| user_002 | Standing | 1.446309440458e12 | -3.10335 | -9.19628 | -0.920286 |
| user_002 | Standing | 1.446309440467e12 | -3.10335 | -9.00468 | -0.767005 |
| user_002 | Standing | 1.446309440474e12 | -3.02671 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309440482e12 | -3.10335 | -8.92803 | -0.805325 |
| user_002 | Standing | 1.446309440491e12 | -3.17999 | -8.92803 | -0.843646 |
| user_002 | Standing | 1.446309440498e12 | -3.33327 | -8.96635 | -0.805325 |
| user_002 | Standing | 1.446309440506e12 | -3.29495 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440515e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440523e12 | -3.29495 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.446309440531e12 | -3.37159 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309440539e12 | -3.33327 | -8.92803 | -0.613724 |
| user_002 | Standing | 1.446309440547e12 | -3.33327 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440555e12 | -3.21831 | -9.08132 | -0.690364 |
| user_002 | Standing | 1.446309440564e12 | -3.06503 | -8.88971 | -0.652044 |
| user_002 | Standing | 1.446309440571e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.44630944058e12 | -3.06503 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440587e12 | -3.10335 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440596e12 | -3.06503 | -9.08132 | -0.613724 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440612e12 | -2.95007 | -9.08132 | -0.728685 |
| user_002 | Standing | 1.44630944062e12 | -3.14167 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309440628e12 | -3.02671 | -8.92803 | -0.537083 |
| user_002 | Standing | 1.446309440636e12 | -3.14167 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309440645e12 | -3.10335 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.446309440653e12 | -3.14167 | -8.81307 | -0.307161 |
| user_002 | Standing | 1.44630944066e12 | -3.17999 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309440669e12 | -3.21831 | -8.85139 | -0.1922 |
| user_002 | Standing | 1.446309440676e12 | -3.17999 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309440685e12 | -3.02671 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309440693e12 | -3.21831 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309440701e12 | -3.10335 | -9.08132 | -0.498763 |
| user_002 | Standing | 1.446309440709e12 | -3.29495 | -8.96635 | -0.537083 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309440725e12 | -3.10335 | -9.15796 | -0.498763 |
| user_002 | Standing | 1.446309440734e12 | -2.91174 | -9.00468 | -0.345482 |
| user_002 | Standing | 1.446309440741e12 | -3.17999 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440749e12 | -3.21831 | -9.08132 | -0.537083 |
| user_002 | Standing | 1.446309440757e12 | -3.06503 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440766e12 | -3.06503 | -9.00468 | -0.613724 |
| user_002 | Standing | 1.446309440774e12 | -3.10335 | -8.92803 | -0.498763 |
| user_002 | Standing | 1.446309440782e12 | -3.25663 | -8.92803 | -0.613724 |
| user_002 | Standing | 1.44630944079e12 | -3.17999 | -8.92803 | -0.728685 |
| user_002 | Standing | 1.446309440798e12 | -3.29495 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309440806e12 | -3.17999 | -9.08132 | -0.422122 |
| user_002 | Standing | 1.446309440814e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440822e12 | -3.06503 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440831e12 | -3.17999 | -9.04299 | -0.537083 |
| user_002 | Standing | 1.446309440839e12 | -3.14167 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309440847e12 | -3.17999 | -9.08132 | -0.345482 |
| user_002 | Standing | 1.446309440855e12 | -3.21831 | -9.08132 | -0.613724 |
| user_002 | Standing | 1.446309440863e12 | -3.33327 | -9.15796 | -0.460442 |
| user_002 | Standing | 1.446309440871e12 | -3.37159 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440879e12 | -3.17999 | -8.92803 | -0.537083 |
| user_002 | Standing | 1.446309440887e12 | -3.17999 | -8.96635 | -0.345482 |
| user_002 | Standing | 1.446309440896e12 | -3.17999 | -9.04299 | -0.383802 |
| user_002 | Standing | 1.446309440904e12 | -3.37159 | -9.11964 | -0.345482 |
| user_002 | Standing | 1.446309440912e12 | -3.33327 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.44630944092e12 | -3.44823 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440927e12 | -3.25663 | -8.81307 | -0.345482 |
| user_002 | Standing | 1.446309440936e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309440944e12 | -3.40991 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440951e12 | -3.21831 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.44630944096e12 | -3.33327 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309440968e12 | -3.37159 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 |
| user_002 | Standing | 1.446309440985e12 | -2.98839 | -8.88971 | -0.575403 |
| user_002 | Standing | 1.446309440992e12 | -3.06503 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309441001e12 | -2.87342 | -8.88971 | -0.652044 |
| user_002 | Standing | 1.446309441009e12 | -3.06503 | -9.11964 | -0.805325 |
| user_002 | Standing | 1.446309441017e12 | -2.91174 | -9.08132 | -0.652044 |
| user_002 | Standing | 1.446309441025e12 | -2.87342 | -9.00468 | -0.498763 |
| user_002 | Standing | 1.446309441033e12 | -2.91174 | -9.15796 | -0.422122 |
| user_002 | Standing | 1.446309441041e12 | -2.91174 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441049e12 | -2.87342 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309441057e12 | -3.02671 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441065e12 | -3.02671 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309441073e12 | -3.06503 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309441081e12 | -2.95007 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.44630944109e12 | -3.06503 | -9.04299 | -0.613724 |
| user_002 | Standing | 1.446309441098e12 | -2.95007 | -9.11964 | -0.498763 |
| user_002 | Standing | 1.446309441106e12 | -2.98839 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441113e12 | -2.98839 | -9.11964 | -0.498763 |
| user_002 | Standing | 1.446309441121e12 | -2.98839 | -9.15796 | -0.613724 |
| user_002 | Standing | 1.44630944113e12 | -3.14167 | -9.08132 | -0.575403 |
| user_002 | Standing | 1.446309441138e12 | -3.10335 | -9.15796 | -0.613724 |
| user_002 | Standing | 1.446309441146e12 | -2.98839 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441154e12 | -3.17999 | -9.00468 | -0.537083 |
| user_002 | Standing | 1.446309441162e12 | -3.06503 | -8.85139 | -0.537083 |
| user_002 | Standing | 1.44630944117e12 | -3.25663 | -9.11964 | -0.460442 |
| user_002 | Standing | 1.446309441179e12 | -3.29495 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441186e12 | -3.33327 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441195e12 | -3.17999 | -8.96635 | -0.575403 |
| user_002 | Standing | 1.446309441202e12 | -3.21831 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441211e12 | -3.52487 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441219e12 | -3.40991 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309441226e12 | -3.37159 | -8.77475 | -0.498763 |
| user_002 | Standing | 1.446309441235e12 | -3.48655 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441243e12 | -3.29495 | -8.96635 | -0.652044 |
| user_002 | Standing | 1.446309441252e12 | -3.25663 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.44630944126e12 | -3.37159 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309441268e12 | -3.14167 | -8.85139 | -0.690364 |
| user_002 | Standing | 1.446309441276e12 | -3.29495 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441284e12 | -3.37159 | -9.11964 | -0.575403 |
| user_002 | Standing | 1.446309441292e12 | -3.33327 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.4463094413e12 | -3.25663 | -8.77475 | -0.460442 |
| user_002 | Standing | 1.446309441308e12 | -3.40991 | -8.65979 | -0.345482 |
| user_002 | Standing | 1.446309441316e12 | -3.17999 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309441324e12 | -3.44823 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309441332e12 | -3.25663 | -8.73643 | -0.383802 |
| user_002 | Standing | 1.446309441341e12 | -3.37159 | -9.08132 | -0.460442 |
| user_002 | Standing | 1.446309441349e12 | -3.44823 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441357e12 | -3.25663 | -8.92803 | -0.230521 |
| user_002 | Standing | 1.446309441365e12 | -3.25663 | -8.81307 | -0.383802 |
| user_002 | Standing | 1.446309441373e12 | -3.44823 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309441381e12 | -3.40991 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441389e12 | -3.33327 | -8.92803 | -0.345482 |
| user_002 | Standing | 1.446309441397e12 | -3.52487 | -8.73643 | -0.307161 |
| user_002 | Standing | 1.446309441405e12 | -3.40991 | -8.81307 | -0.1922 |
| user_002 | Standing | 1.446309441413e12 | -3.29495 | -9.00468 | -0.307161 |
| user_002 | Standing | 1.446309441421e12 | -3.33327 | -9.04299 | -0.1922 |
| user_002 | Standing | 1.44630944143e12 | -3.29495 | -8.88971 | -0.230521 |
| user_002 | Standing | 1.446309441437e12 | -3.17999 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441445e12 | -3.37159 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309441453e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441462e12 | -3.44823 | -8.85139 | -0.383802 |
| user_002 | Standing | 1.44630944147e12 | -3.25663 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441478e12 | -3.37159 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309441486e12 | -3.29495 | -8.88971 | -0.345482 |
| user_002 | Standing | 1.446309441494e12 | -3.17999 | -8.92803 | -0.345482 |
| user_002 | Standing | 1.446309441501e12 | -3.29495 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.44630944151e12 | -3.14167 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441518e12 | -3.17999 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441527e12 | -3.06503 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441535e12 | -3.33327 | -8.81307 | -0.613724 |
| user_002 | Standing | 1.446309441543e12 | -3.10335 | -8.92803 | -0.383802 |
| user_002 | Standing | 1.446309441551e12 | -3.29495 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309441558e12 | -3.21831 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441567e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441575e12 | -3.17999 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441583e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441591e12 | -3.40991 | -8.73643 | -0.230521 |
| user_002 | Standing | 1.446309441599e12 | -3.44823 | -8.92803 | -0.498763 |
| user_002 | Standing | 1.446309441607e12 | -3.25663 | -8.81307 | -0.383802 |
| user_002 | Standing | 1.446309441615e12 | -3.40991 | -8.77475 | -0.345482 |
| user_002 | Standing | 1.446309441623e12 | -3.29495 | -8.69811 | -0.307161 |
| user_002 | Standing | 1.446309441632e12 | -3.25663 | -8.65979 | -0.460442 |
| user_002 | Standing | 1.446309441639e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441648e12 | -3.17999 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309441655e12 | -3.06503 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441664e12 | -3.17999 | -9.2346 | -0.460442 |
| user_002 | Standing | 1.446309441672e12 | -3.10335 | -9.11964 | -0.422122 |
| user_002 | Standing | 1.44630944168e12 | -3.10335 | -9.19628 | -0.345482 |
| user_002 | Standing | 1.446309441688e12 | -3.29495 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441696e12 | -3.21831 | -8.96635 | -0.307161 |
| user_002 | Standing | 1.446309441705e12 | -3.14167 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441713e12 | -3.25663 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309441721e12 | -3.25663 | -9.00468 | -0.575403 |
| user_002 | Standing | 1.446309441729e12 | -3.29495 | -9.04299 | -0.345482 |
| user_002 | Standing | 1.446309441737e12 | -3.25663 | -9.00468 | -0.575403 |
| user_002 | Standing | 1.446309441745e12 | -3.17999 | -8.96635 | -0.537083 |
| user_002 | Standing | 1.446309441752e12 | -3.25663 | -9.11964 | -0.422122 |
| user_002 | Standing | 1.446309441761e12 | -3.21831 | -9.11964 | -0.307161 |
| user_002 | Standing | 1.446309441769e12 | -3.10335 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441777e12 | -3.10335 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441785e12 | -3.21831 | -9.11964 | -0.345482 |
| user_002 | Standing | 1.446309441793e12 | -3.17999 | -9.2346 | -0.307161 |
| user_002 | Standing | 1.446309441802e12 | -3.17999 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.446309441809e12 | -3.17999 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309441818e12 | -3.06503 | -9.04299 | -0.383802 |
| user_002 | Standing | 1.446309441825e12 | -3.14167 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441833e12 | -3.25663 | -8.88971 | -0.422122 |
| user_002 | Standing | 1.446309441842e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.44630944185e12 | -3.21831 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441858e12 | -3.02671 | -8.81307 | -0.460442 |
| user_002 | Standing | 1.446309441867e12 | -3.17999 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441875e12 | -3.06503 | -8.85139 | -0.268841 |
| user_002 | Standing | 1.446309441882e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441891e12 | -3.33327 | -8.88971 | -0.268841 |
| user_002 | Standing | 1.446309441899e12 | -3.33327 | -8.81307 | -0.1922 |
| user_002 | Standing | 1.446309441907e12 | -3.17999 | -8.77475 | -0.230521 |
| user_002 | Standing | 1.446309441915e12 | -3.17999 | -9.08132 | -3.8919e-2 |
| user_002 | Standing | 1.446309441923e12 | -3.14167 | -9.19628 | -0.15388 |
| user_002 | Standing | 1.446309441931e12 | -3.21831 | -9.08132 | -0.1922 |
| user_002 | Standing | 1.446309441939e12 | -3.06503 | -9.00468 | -0.268841 |
| user_002 | Standing | 1.446309441947e12 | -3.29495 | -8.92803 | -0.11556 |
| user_002 | Standing | 1.446309441956e12 | -3.21831 | -9.00468 | -0.307161 |
| user_002 | Standing | 1.446309441964e12 | -3.06503 | -8.88971 | -0.268841 |
| user_002 | Standing | 1.446309441971e12 | -3.25663 | -9.00468 | -0.15388 |
| user_002 | Standing | 1.446309441979e12 | -3.25663 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309441987e12 | -3.29495 | -8.92803 | -0.1922 |
| user_002 | Standing | 1.446309441995e12 | -3.33327 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309442004e12 | -3.21831 | -8.88971 | -0.11556 |
| user_002 | Standing | 1.446309442012e12 | -3.25663 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.44630944202e12 | -3.06503 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442028e12 | -3.06503 | -9.11964 | -3.8919e-2 |
| user_002 | Standing | 1.446309442036e12 | -3.02671 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309442045e12 | -2.95007 | -9.19628 | -0.15388 |
| user_002 | Standing | 1.446309442052e12 | -2.91174 | -9.04299 | -0.1922 |
| user_002 | Standing | 1.446309442061e12 | -2.91174 | -9.19628 | -5.99e-4 |
| user_002 | Standing | 1.446309442068e12 | -2.95007 | -9.08132 | -5.99e-4 |
| user_002 | Standing | 1.446309442077e12 | -3.10335 | -9.08132 | -7.7239e-2 |
| user_002 | Standing | 1.446309442085e12 | -2.98839 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442092e12 | -3.10335 | -9.15796 | -0.268841 |
| user_002 | Standing | 1.446309442157e12 | -3.02671 | -8.92803 | -7.7239e-2 |
| user_002 | Standing | 1.446309442223e12 | -3.14167 | -8.92803 | -3.8919e-2 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442352e12 | -2.72014 | -9.11964 | -7.7239e-2 |
| user_002 | Standing | 1.446309442417e12 | -2.56686 | -9.19628 | -3.8919e-2 |
| user_002 | Standing | 1.446309442481e12 | -2.6435 | -9.11964 | 3.7722e-2 |
| user_002 | Standing | 1.446309442547e12 | -2.56686 | -9.11964 | -7.7239e-2 |
| user_002 | Standing | 1.446309442611e12 | -2.60518 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442675e12 | -2.4519 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.44630944274e12 | -2.41358 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442805e12 | -2.41358 | -9.08132 | -7.7239e-2 |
| user_002 | Standing | 1.44630944287e12 | -2.33694 | -9.15796 | -7.7239e-2 |
| user_002 | Standing | 1.446309442935e12 | -2.37526 | -9.15796 | -5.99e-4 |
| user_002 | Standing | 1.446309443e12 | -2.29862 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309443064e12 | -2.2603 | -9.2346 | -0.11556 |
| user_002 | Standing | 1.446309443129e12 | -2.22198 | -9.2346 | -0.11556 |
| user_002 | Standing | 1.446309443194e12 | -2.10702 | -9.27292 | -3.8919e-2 |
| user_002 | Standing | 1.446309443258e12 | -2.18366 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443323e12 | -2.2603 | -9.19628 | -0.11556 |
| user_002 | Standing | 1.446309443388e12 | -2.2603 | -9.15796 | -5.99e-4 |
| user_002 | Standing | 1.446309443453e12 | -2.10702 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.446309443517e12 | -2.03038 | -9.31124 | -3.8919e-2 |
| user_002 | Standing | 1.446309443582e12 | -2.14534 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443647e12 | -2.29862 | -9.19628 | -5.99e-4 |
| user_002 | Standing | 1.446309443711e12 | -2.29862 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309443776e12 | -2.18366 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443841e12 | -2.10702 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309443906e12 | -2.10702 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443971e12 | -2.29862 | -9.15796 | 0.114362 |
| user_002 | Standing | 1.446309444036e12 | -2.03038 | -9.27292 | -5.99e-4 |
| user_002 | Standing | 1.4463094441e12 | -2.10702 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309444165e12 | -2.14534 | -9.2346 | 3.7722e-2 |
| user_002 | Standing | 1.44630944423e12 | -2.22198 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309444295e12 | -2.33694 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309444359e12 | -2.10702 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309444424e12 | -2.0687 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.446309444489e12 | -2.03038 | -9.2346 | 0.114362 |
| user_002 | Standing | 1.446309444554e12 | -2.41358 | -9.00468 | 0.191003 |
| user_002 | Standing | 1.446309444618e12 | -2.37526 | -9.19628 | 0.267643 |
| user_002 | Standing | 1.446309444683e12 | -2.14534 | -9.2346 | 7.6042e-2 |
| user_002 | Standing | 1.446309444748e12 | -2.0687 | -9.27292 | 7.6042e-2 |
| user_002 | Standing | 1.446309444812e12 | -2.33694 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309444877e12 | -2.2603 | -9.19628 | 0.229323 |
| user_002 | Standing | 1.446309444941e12 | -2.18366 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309445007e12 | -2.22198 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309445072e12 | -2.22198 | -9.15796 | 0.191003 |
| user_002 | Standing | 1.446309445137e12 | -2.10702 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309445202e12 | -2.10702 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309445265e12 | -1.91542 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309445331e12 | -1.83878 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309445395e12 | -1.91542 | -9.08132 | 0.305964 |
| user_002 | Standing | 1.446309445459e12 | -1.95374 | -9.19628 | 0.152682 |
| user_002 | Standing | 1.446309445525e12 | -1.83878 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.44630944559e12 | -1.80046 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309445654e12 | -1.68549 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446309445719e12 | -1.80046 | -9.19628 | 0.267643 |
| user_002 | Standing | 1.446309445784e12 | -1.76214 | -9.34956 | -5.99e-4 |
| user_002 | Standing | 1.446309445849e12 | -1.91542 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309445913e12 | -1.80046 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309445978e12 | -1.76214 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309446043e12 | -1.83878 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309446108e12 | -1.83878 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309446173e12 | -1.8771 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309446237e12 | -1.8771 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309446302e12 | -1.8771 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309446367e12 | -1.8771 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309446432e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309446496e12 | -1.91542 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309446561e12 | -1.8771 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309446625e12 | -1.83878 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309446691e12 | -1.91542 | -9.27292 | 7.6042e-2 |
| user_002 | Standing | 1.446309446755e12 | -1.8771 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.44630944682e12 | -1.80046 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309446884e12 | -1.80046 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.44630944695e12 | -1.72382 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447014e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447079e12 | -1.83878 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309447144e12 | -1.8771 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309447209e12 | -1.76214 | -9.19628 | 0.152682 |
| user_002 | Standing | 1.446309447273e12 | -1.72382 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309447338e12 | -1.83878 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309447403e12 | -1.8771 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309447467e12 | -1.83878 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447532e12 | -1.68549 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.446309447597e12 | -1.68549 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309447662e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309447726e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309447791e12 | -1.80046 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309447855e12 | -1.64717 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309447921e12 | -1.60885 | -9.27292 | 0.114362 |
| user_002 | Standing | 1.446309447986e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.44630944805e12 | -1.76214 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309448115e12 | -1.72382 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.44630944818e12 | -1.72382 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309448245e12 | -1.64717 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309448309e12 | -1.64717 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309448374e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309448439e12 | -1.72382 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446309448504e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309448569e12 | -1.64717 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309448633e12 | -1.72382 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309448698e12 | -1.68549 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309448763e12 | -1.68549 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309448827e12 | -1.64717 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309448892e12 | -1.64717 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446309448957e12 | -1.60885 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449022e12 | -1.60885 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309449087e12 | -1.68549 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449151e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449216e12 | -1.68549 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309449281e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449346e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.44630944941e12 | -1.64717 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446309449475e12 | -1.76214 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.44630944954e12 | -1.64717 | -9.31124 | -5.99e-4 |
| user_002 | Standing | 1.446309449605e12 | -1.64717 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449669e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449734e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449799e12 | -1.76214 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309449864e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449928e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449993e12 | -1.72382 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450057e12 | -1.72382 | -9.34956 | 0.114362 |
| user_002 | Standing | 1.446309450123e12 | -1.72382 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309450188e12 | -1.68549 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450252e12 | -1.83878 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309450317e12 | -1.68549 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309450381e12 | -1.72382 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309450446e12 | -1.68549 | -9.34956 | 3.7722e-2 |
| user_002 | Standing | 1.446309450511e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309450576e12 | -1.76214 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309450641e12 | -1.72382 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309450705e12 | -1.64717 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.44630945077e12 | -1.72382 | -9.34956 | 0.114362 |
| user_002 | Standing | 1.446309450835e12 | -1.83878 | -9.2346 | 0.191003 |
dataDF.count()
res5: Long = 13679
dataDF.select($"user_id").distinct().show()
+--------+
| user_id|
+--------+
|user_002|
|user_006|
|user_005|
|user_001|
|user_007|
|user_003|
|user_004|
+--------+
dataDF.select($"activity").distinct().show()
+--------+
|activity|
+--------+
| Sitting|
| Walking|
| Jumping|
|Standing|
| Jogging|
+--------+
val sDF = dataDF.sample(false,0.105, 12345L)
sDF.count
sDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 4 more fields]
res8: Long = 1427
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong| x| y| z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping| 1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping| 1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping| 1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping| 1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping| 1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong| x| y| z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping| 1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping| 1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping| 1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping| 1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping| 1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
display(dataDF.sample(false,0.1))
| user_id | activity | timeStampAsLong | x | y | z |
|---|---|---|---|---|---|
| user_001 | Jumping | 1.446047227995e12 | 1.57173 | -5.86241 | -3.75599 |
| user_001 | Jumping | 1.446047228578e12 | -1.07237 | -1.95374 | 0.191003 |
| user_001 | Jumping | 1.446047228771e12 | 5.63368 | -19.58108 | 5.59417 |
| user_001 | Jumping | 1.446047229419e12 | 5.17384 | -19.2362 | 0.612526 |
| user_001 | Jumping | 1.446047230131e12 | 2.6447 | -3.75479 | 0.114362 |
| user_001 | Jumping | 1.446047230713e12 | 5.44208 | -13.56479 | -0.498763 |
| user_001 | Jumping | 1.446047230972e12 | -3.7722e-2 | 1.07357 | -0.958607 |
| user_001 | Jumping | 1.446047231102e12 | 0.805325 | -1.14901 | 1.53221 |
| user_001 | Jumping | 1.446047231878e12 | 3.75599 | -19.27452 | -1.76333 |
| user_001 | Jumping | 1.446047232655e12 | 4.48408 | -14.3312 | -0.498763 |
| user_001 | Jumping | 1.446047233044e12 | 2.37646 | -3.29495 | -1.45677 |
| user_001 | Jumping | 1.446047236863e12 | -0.650847 | -0.305964 | -1.15021 |
| user_001 | Jumping | 1.446047238223e12 | 2.60638 | -3.40991 | -0.422122 |
| user_001 | Jumping | 1.446047239e12 | 10.50036 | -19.58108 | 1.91542 |
| user_001 | Jumping | 1.446047239647e12 | 9.58068 | -19.58108 | 1.53221 |
| user_001 | Jumping | 1.446047240166e12 | 3.29615 | -3.98471 | -0.422122 |
| user_001 | Jumping | 1.446047241007e12 | 9.35075 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047241072e12 | 5.71033 | -15.74905 | -1.15021 |
| user_001 | Jumping | 1.446047241395e12 | -0.382604 | -0.842448 | 0.382604 |
| user_001 | Jumping | 1.446047241459e12 | -0.765807 | -1.41725 | 7.6042e-2 |
| user_001 | Jumping | 1.446047241977e12 | 0.15388 | -0.919089 | 2.18366 |
| user_001 | Jumping | 1.446047242042e12 | -0.995729 | 0.460442 | 0.305964 |
| user_001 | Jumping | 1.446047242495e12 | 0.11556 | 0.613724 | -1.30349 |
| user_001 | Jumping | 1.446047242819e12 | 5.32712 | -13.21991 | -1.34181 |
| user_001 | Jumping | 1.446047243401e12 | 0.537083 | -1.26397 | 0.535886 |
| user_001 | Jumping | 1.446047243855e12 | -0.535886 | -0.957409 | 1.18733 |
| user_001 | Jumping | 1.44604724392e12 | 3.60271 | -0.114362 | 0.574206 |
| user_003 | Jumping | 1.446047779006e12 | -0.574206 | -9.00468 | -1.76333 |
| user_003 | Jumping | 1.446047779071e12 | -3.14167 | -4.36792 | -1.68669 |
| user_003 | Jumping | 1.446047779783e12 | -1.72382 | -13.98632 | -4.10087 |
| user_003 | Jumping | 1.446047781013e12 | -3.06503 | -6.70546 | -1.76333 |
| user_003 | Jumping | 1.446047781272e12 | -0.689167 | -5.55585 | -2.37646 |
| user_003 | Jumping | 1.446047782373e12 | -1.64717 | -5.90073 | -2.33814 |
| user_003 | Jumping | 1.446047782632e12 | -3.40991 | -19.54276 | -5.71033 |
| user_003 | Jumping | 1.44604778328e12 | -2.41358 | -5.59417 | -0.613724 |
| user_003 | Jumping | 1.446047784834e12 | -2.4519 | 1.22685 | -1.41845 |
| user_003 | Jumping | 1.446047784964e12 | -0.842448 | 0.422122 | -0.996927 |
| user_003 | Jumping | 1.446047785741e12 | -5.67081 | -19.58108 | -6.59169 |
| user_003 | Jumping | 1.446047786388e12 | -1.41725 | -8.85139 | -1.38013 |
| user_003 | Jumping | 1.446047787489e12 | -1.26397 | -9.34956 | -3.06622 |
| user_003 | Jumping | 1.446047787553e12 | -3.44823 | -6.47553 | -2.75966 |
| user_003 | Jumping | 1.446047787812e12 | -2.22198 | -5.24928 | -2.0699 |
| user_003 | Jumping | 1.446047788136e12 | -3.14167 | 2.72134 | -0.575403 |
| user_003 | Jumping | 1.44604778859e12 | -1.83878 | -9.77108 | -3.25783 |
| user_003 | Jumping | 1.446047788654e12 | -3.14167 | -6.55217 | -2.6447 |
| user_003 | Jumping | 1.446047789366e12 | -0.689167 | 0.230521 | -0.728685 |
| user_003 | Jumping | 1.44604778956e12 | -3.29495 | -18.96796 | -5.05888 |
| user_003 | Jumping | 1.446047789625e12 | -2.22198 | -12.0703 | -3.98591 |
| user_003 | Jumping | 1.446047790337e12 | 1.15021 | -0.919089 | 1.64717 |
| user_003 | Jumping | 1.446047794093e12 | -2.10702 | -5.93905 | -2.14654 |
| user_003 | Jumping | 1.446047794416e12 | 5.99e-4 | -10.76741 | -2.33814 |
| user_002 | Standing | 1.446309439406e12 | -2.8351 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439414e12 | -2.8351 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439609e12 | -2.6435 | -9.04299 | -0.996927 |
| user_002 | Standing | 1.446309439649e12 | -2.60518 | -9.11964 | -0.843646 |
| user_002 | Standing | 1.446309439803e12 | -2.87342 | -8.85139 | -0.843646 |
| user_002 | Standing | 1.446309439892e12 | -3.02671 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440054e12 | -3.10335 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309440337e12 | -3.14167 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440394e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440474e12 | -3.02671 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.44630944058e12 | -3.06503 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440587e12 | -3.10335 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440645e12 | -3.10335 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.44630944066e12 | -3.17999 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309440822e12 | -3.06503 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440936e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.44630944096e12 | -3.33327 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309441041e12 | -2.91174 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441154e12 | -3.17999 | -9.00468 | -0.537083 |
| user_002 | Standing | 1.44630944117e12 | -3.25663 | -9.11964 | -0.460442 |
| user_002 | Standing | 1.446309441349e12 | -3.44823 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441357e12 | -3.25663 | -8.92803 | -0.230521 |
| user_002 | Standing | 1.446309441373e12 | -3.44823 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309441381e12 | -3.40991 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441437e12 | -3.17999 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441462e12 | -3.44823 | -8.85139 | -0.383802 |
| user_002 | Standing | 1.446309441639e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441655e12 | -3.06503 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441696e12 | -3.21831 | -8.96635 | -0.307161 |
| user_002 | Standing | 1.446309441793e12 | -3.17999 | -9.2346 | -0.307161 |
| user_002 | Standing | 1.446309442012e12 | -3.25663 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442068e12 | -2.95007 | -9.08132 | -5.99e-4 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309443258e12 | -2.18366 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443582e12 | -2.14534 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443841e12 | -2.10702 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309445072e12 | -2.22198 | -9.15796 | 0.191003 |
| user_002 | Standing | 1.44630944559e12 | -1.80046 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309446755e12 | -1.8771 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309447403e12 | -1.8771 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309447726e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.44630944805e12 | -1.76214 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309448698e12 | -1.68549 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309449216e12 | -1.68549 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309449928e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309450446e12 | -1.68549 | -9.34956 | 3.7722e-2 |
| user_002 | Standing | 1.446309451806e12 | -1.68549 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309452907e12 | -1.72382 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309453101e12 | -1.76214 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309454591e12 | -1.72382 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.44630945485e12 | -1.80046 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309455045e12 | -1.68549 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309456081e12 | -1.72382 | -9.31124 | 0.229323 |
| user_005 | Standing | 1.446046600265e12 | 0.268841 | -9.46452 | 0.305964 |
| user_005 | Standing | 1.446046601237e12 | 0.307161 | -9.4262 | 0.650847 |
| user_005 | Standing | 1.446046602725e12 | 0.345482 | -9.4262 | 0.459245 |
| user_005 | Standing | 1.44604660402e12 | 0.307161 | -9.34956 | 0.459245 |
| user_005 | Standing | 1.446046605509e12 | 0.345482 | -9.46452 | 0.459245 |
| user_005 | Standing | 1.446046606026e12 | 0.345482 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.446046606867e12 | 0.268841 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.446046607386e12 | 0.307161 | -9.4262 | 0.420925 |
| user_005 | Standing | 1.44604660758e12 | 0.345482 | -9.4262 | 0.382604 |
| user_005 | Standing | 1.446046607903e12 | 0.383802 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.44604660868e12 | 0.307161 | -9.46452 | 0.420925 |
| user_005 | Standing | 1.446046608939e12 | 0.307161 | -9.46452 | 0.420925 |
| user_005 | Standing | 1.446046610557e12 | 0.345482 | -9.4262 | 0.382604 |
| user_005 | Standing | 1.446046610816e12 | 0.307161 | -9.34956 | 0.344284 |
| user_005 | Standing | 1.446046610881e12 | 0.268841 | -9.4262 | 0.382604 |
| user_005 | Standing | 1.446046611982e12 | 0.307161 | -9.46452 | 0.382604 |
| user_005 | Standing | 1.44604661237e12 | 0.307161 | -9.38788 | 0.382604 |
| user_005 | Standing | 1.446046614442e12 | 0.460442 | -9.38788 | 0.344284 |
| user_005 | Standing | 1.446046615218e12 | 0.230521 | -9.4262 | 0.229323 |
| user_005 | Standing | 1.446046615607e12 | 0.307161 | -9.54116 | 0.191003 |
| user_002 | Sitting | 1.44603456478e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.44603456491e12 | -0.804128 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034565169e12 | -0.842448 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034566723e12 | -0.727487 | -0.305964 | 9.4262 |
| user_002 | Sitting | 1.446034566853e12 | -0.804128 | -3.7722e-2 | 9.38788 |
| user_002 | Sitting | 1.446034567047e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034567111e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034567565e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034568795e12 | -0.689167 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.44603456886e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034569701e12 | -0.804128 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034570673e12 | -0.765807 | -0.229323 | 9.38788 |
| user_002 | Sitting | 1.446034571384e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034571902e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034572097e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034573262e12 | -0.765807 | -0.114362 | 9.46452 |
| user_002 | Sitting | 1.446034573521e12 | -0.727487 | -0.114362 | 9.46452 |
| user_002 | Sitting | 1.446034574169e12 | -0.727487 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034574298e12 | -0.689167 | -0.152682 | 9.50284 |
| user_002 | Sitting | 1.446034574816e12 | -0.727487 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034576693e12 | -0.804128 | -0.152682 | 9.38788 |
| user_002 | Sitting | 1.446034577536e12 | -0.804128 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.4460345776e12 | -0.727487 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034578571e12 | -0.765807 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034578766e12 | -0.765807 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.44603457896e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034579025e12 | -0.727487 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034579672e12 | -0.727487 | -0.152682 | 9.38788 |
| user_002 | Sitting | 1.446034579737e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034580125e12 | -0.727487 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034580838e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034580903e12 | -0.804128 | -0.229323 | 9.46452 |
| user_002 | Sitting | 1.446034581291e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034581356e12 | -0.689167 | -0.114362 | 9.4262 |
| user_006 | Jogging | 1.446046855501e12 | -2.6435 | -0.612526 | -2.0699 |
| user_006 | Jogging | 1.446046855761e12 | -6.85874 | -4.86608 | -2.60638 |
| user_006 | Jogging | 1.446046855826e12 | -0.765807 | 0.575403 | -0.996927 |
| user_006 | Jogging | 1.446046856085e12 | -10.46085 | -11.34221 | -4.29247 |
| user_006 | Jogging | 1.446046856797e12 | -10.42253 | -11.61046 | 1.18733 |
| user_006 | Jogging | 1.44604685738e12 | -1.03405 | 0.920286 | -1.64837 |
| user_006 | Jogging | 1.44604685848e12 | -4.36792 | -7.58682 | -2.6447 |
| user_006 | Jogging | 1.446046860034e12 | -5.55585 | -0.305964 | -1.49509 |
| user_006 | Jogging | 1.446046861523e12 | -2.56686 | -6.09233 | -1.07357 |
| user_006 | Jogging | 1.446046862236e12 | -2.52854 | -0.995729 | -0.652044 |
| user_006 | Jogging | 1.446046865731e12 | -7.85507 | -10.69077 | -2.49142 |
| user_006 | Jogging | 1.44604686612e12 | -6.82042 | -6.39889 | -3.79431 |
| user_006 | Jogging | 1.446046867544e12 | -5.90073 | -16.9753 | -4.13919 |
| user_006 | Jogging | 1.446046868515e12 | -0.650847 | 0.422122 | -0.805325 |
| user_006 | Jogging | 1.446046868579e12 | -3.98471 | -7.89339 | -4.82896 |
| user_006 | Jogging | 1.446046869162e12 | -6.43721 | -5.2876 | -3.06622 |
| user_006 | Jogging | 1.446046870715e12 | -2.4519 | -0.612526 | -1.15021 |
| user_006 | Jogging | 1.44604687091e12 | -9.84772 | -14.67608 | 1.07237 |
| user_005 | Jogging | 1.446046533201e12 | 7.05154 | -19.58108 | -4.36911 |
| user_005 | Jogging | 1.446046533718e12 | -1.60885 | -1.11069 | -6.89825 |
| user_005 | Jogging | 1.446046534949e12 | 8.54603 | -10.11596 | 8.39155 |
| user_005 | Jogging | 1.446046535402e12 | -4.02303 | -11.57214 | 0.420925 |
| user_005 | Jogging | 1.446046535855e12 | -1.72382 | -1.03405 | -6.36177 |
| user_005 | Jogging | 1.446046536632e12 | -9.19628 | 1.22685 | -4.56072 |
| user_005 | Jogging | 1.446046536696e12 | -1.45557 | -3.79311 | -0.613724 |
| user_005 | Jogging | 1.446046537118e12 | 1.49509 | -5.67081 | 12.79839 |
| user_005 | Jogging | 1.446046537191e12 | -8.88971 | -19.58108 | 8.58315 |
| user_005 | Jogging | 1.446046537417e12 | -2.75846 | -13.18159 | 6.66714 |
| user_005 | Jogging | 1.446046537522e12 | -3.86975 | -19.58108 | -4.94392 |
| user_005 | Jogging | 1.446046537854e12 | 1.64837 | -14.36952 | 2.68182 |
| user_005 | Jogging | 1.446046537976e12 | 5.86361 | -5.47921 | -8.27779 |
| user_005 | Jogging | 1.446046538243e12 | -2.2603 | -19.58108 | -6.09353 |
| user_005 | Jogging | 1.446046538259e12 | -4.09967 | -17.24354 | -0.881966 |
| user_005 | Jogging | 1.446046538307e12 | 4.9056 | 10.04052 | 3.06503 |
| user_005 | Jogging | 1.446046538364e12 | 2.10822 | 6.47673 | 4.98104 |
| user_005 | Jogging | 1.446046538461e12 | 0.690364 | -14.17792 | -11.30509 |
| user_005 | Jogging | 1.446046538534e12 | 6.9749 | -8.46819 | 8.12331 |
| user_005 | Jogging | 1.446046538672e12 | 1.07357 | -12.98999 | -7.85626 |
| user_005 | Jogging | 1.446046538736e12 | -14.1396 | -1.07237 | -8.73763 |
| user_005 | Jogging | 1.446046538947e12 | -3.94639 | -19.58108 | -8.85259 |
| user_005 | Jogging | 1.446046539003e12 | 2.14654 | 5.44208 | 5.096 |
| user_005 | Jogging | 1.446046539019e12 | 6.89825 | 12.87622 | 6.70546 |
| user_005 | Jogging | 1.44604653927e12 | -2.79678 | -9.96268 | 6.28393 |
| user_005 | Jogging | 1.446046539384e12 | 12.60798 | -10.42253 | -7.9329 |
| user_005 | Jogging | 1.446046539416e12 | -7.70178 | -0.957409 | -13.33607 |
| user_005 | Jogging | 1.446046539465e12 | -10.001 | -1.45557 | -4.10087 |
| user_005 | Jogging | 1.446046539488e12 | -0.305964 | -5.86241 | -7.62634 |
| user_005 | Jogging | 1.446046539603e12 | 17.47466 | -19.58108 | -0.767005 |
| user_005 | Jogging | 1.446046539788e12 | 1.95493 | 1.41845 | 1.99206 |
| user_005 | Jogging | 1.446046539829e12 | -0.765807 | -4.9044 | -7.24314 |
| user_005 | Jogging | 1.446046539942e12 | 0.537083 | -9.27292 | 11.76374 |
| user_005 | Jogging | 1.446046539982e12 | -0.842448 | -16.63042 | 2.75846 |
| user_005 | Jogging | 1.446046540015e12 | -9.00468 | -19.58108 | -5.2888 |
| user_005 | Jogging | 1.446046540056e12 | 1.87829 | -15.97897 | -11.91822 |
| user_005 | Jogging | 1.446046540088e12 | 12.95286 | -2.95007 | -4.48408 |
| user_005 | Jogging | 1.446046540153e12 | -12.98999 | -3.79311 | -1.91661 |
| user_005 | Jogging | 1.446046540161e12 | -9.69444 | -3.14167 | -4.9056 |
| user_005 | Jogging | 1.446046540233e12 | -4.44456 | -11.53382 | 4.82776 |
| user_005 | Jogging | 1.446046540355e12 | -6.85874 | -19.58108 | -4.63736 |
| user_005 | Jogging | 1.446046540363e12 | -8.04667 | -18.39315 | -0.345482 |
| user_005 | Jogging | 1.446046540522e12 | -0.305964 | -7.66346 | -11.42005 |
| user_005 | Jogging | 1.446046540538e12 | 1.15021 | -16.5921 | -19.54396 |
| user_005 | Jogging | 1.446046540756e12 | 6.55337 | -12.03198 | -10.76861 |
| user_005 | Jogging | 1.446046540813e12 | -18.00995 | -3.79311 | -9.15915 |
| user_005 | Jogging | 1.446046540903e12 | -2.8351 | -1.18733 | -3.18118 |
| user_005 | Jogging | 1.446046540911e12 | -3.63983 | -2.49022 | -1.22685 |
| user_005 | Jogging | 1.446046540967e12 | -1.22565 | -19.58108 | -5.05888 |
| user_005 | Jogging | 1.446046541105e12 | 0.690364 | 15.78857 | 11.91702 |
| user_005 | Jogging | 1.446046541218e12 | 0.498763 | -7.89339 | -9.92556 |
| user_005 | Jogging | 1.446046541348e12 | -1.22565 | -12.4535 | 10.69077 |
| user_005 | Jogging | 1.446046541493e12 | -19.0446 | -4.138 | -9.46572 |
| user_005 | Jogging | 1.446046541509e12 | -13.60311 | -2.79678 | -2.79798 |
| user_005 | Jogging | 1.446046541534e12 | -1.03405 | -3.25663 | -8.39275 |
| user_005 | Jogging | 1.446046541542e12 | 0.307161 | -2.68182 | -3.87095 |
| user_005 | Jogging | 1.446046541769e12 | 3.75599 | 18.47099 | 14.1396 |
| user_005 | Jogging | 1.446046541817e12 | -0.382604 | 6.43841 | 8.42987 |
| user_005 | Jogging | 1.446046541922e12 | 9.73396 | -12.68342 | -3.37279 |
| user_005 | Jogging | 1.446046541971e12 | 3.98591 | -17.89499 | 18.54643 |
| user_005 | Jogging | 1.446046542019e12 | 0.15388 | -10.1926 | 10.1926 |
| user_005 | Jogging | 1.446046542092e12 | 5.86361 | -18.92964 | -16.32505 |
| user_005 | Jogging | 1.446046542133e12 | 12.49302 | 2.72134 | -5.2888 |
| user_005 | Jogging | 1.446046542181e12 | -15.97897 | -5.24928 | -11.26677 |
| user_005 | Jogging | 1.446046542238e12 | 1.64837 | -1.07237 | -0.230521 |
| user_005 | Jogging | 1.446046542286e12 | -3.06503 | -7.97003 | 2.60518 |
| user_005 | Jogging | 1.446046542351e12 | 10.27044 | -19.58108 | -3.52607 |
| user_005 | Jogging | 1.446046542359e12 | 10.577 | -19.58108 | 1.60885 |
| user_005 | Jogging | 1.446046542424e12 | -4.40624 | -11.80206 | 6.09233 |
| user_005 | Jogging | 1.446046542521e12 | -0.420925 | 2.98958 | 3.48655 |
| user_005 | Jogging | 1.446046542578e12 | 10.07884 | -19.58108 | -19.58228 |
| user_005 | Jogging | 1.446046542634e12 | -2.03038 | -19.58108 | 0.650847 |
| user_005 | Jogging | 1.446046542667e12 | -0.420925 | -5.47921 | 9.96268 |
| user_005 | Jogging | 1.446046542715e12 | 1.91661 | -16.70706 | 2.72014 |
| user_005 | Jogging | 1.446046542756e12 | 0.11556 | -19.58108 | -15.02216 |
| user_005 | Jogging | 1.44604654278e12 | -0.727487 | -16.09393 | -14.94552 |
| user_005 | Jogging | 1.446046542869e12 | -4.06135 | 2.98958 | -4.13919 |
| user_005 | Jogging | 1.446046542893e12 | -3.98471 | 5.13552 | -2.22318 |
| user_005 | Jogging | 1.446046542982e12 | -7.58682 | -18.31651 | 6.70546 |
| user_005 | Jogging | 1.446046542999e12 | -0.957409 | -19.58108 | -0.652044 |
| user_005 | Jogging | 1.446046543055e12 | 9.12083 | -19.58108 | -7.51138 |
| user_005 | Jogging | 1.446046543128e12 | -0.267643 | -3.10335 | 7.01202 |
| user_005 | Jogging | 1.446046543233e12 | 4.48408 | 1.22685 | -3.37279 |
| user_005 | Jogging | 1.446046543347e12 | 0.383802 | -18.73803 | -1.53341 |
| user_005 | Jogging | 1.446046543427e12 | 7.39642 | -13.87135 | -9.96388 |
| user_005 | Jogging | 1.446046543516e12 | -1.8771 | 2.6447 | -4.75232 |
| user_005 | Jogging | 1.446046543549e12 | -3.71647 | 0.881966 | -0.575403 |
| user_005 | Jogging | 1.446046543637e12 | -0.382604 | -19.58108 | 10.99733 |
| user_005 | Jogging | 1.446046543645e12 | -2.22198 | -19.58108 | 2.56686 |
| user_005 | Jogging | 1.446046543735e12 | 2.33814 | -3.33327 | 2.37526 |
| user_005 | Jogging | 1.44604654398e12 | 9.46572 | -16.89866 | -4.25415 |
| user_005 | Jogging | 1.446046543988e12 | 11.07517 | -16.93698 | -6.28513 |
| user_005 | Jogging | 1.446046544085e12 | -3.02671 | -13.56479 | -2.68302 |
| user_005 | Jogging | 1.446046544093e12 | -3.25663 | -14.29288 | -3.44943 |
| user_005 | Jogging | 1.446046544384e12 | 2.60638 | -19.58108 | -7.77962 |
| user_005 | Jogging | 1.446046544595e12 | 0.767005 | -0.650847 | -2.33814 |
| user_005 | Jogging | 1.446046544627e12 | -1.41725 | -0.880768 | -4.40743 |
| user_005 | Jogging | 1.446046544676e12 | 3.44943 | -17.47346 | 9.4262 |
| user_005 | Jogging | 1.446046544724e12 | 1.68669 | -18.96796 | -5.0972 |
| user_005 | Jogging | 1.44604654504e12 | 6.89825 | -19.58108 | -3.0279 |
| user_005 | Jogging | 1.446046545153e12 | 4.48408 | 2.22318 | 3.33327 |
| user_005 | Jogging | 1.446046545194e12 | 4.36911 | 6.9749 | 5.13432 |
| user_005 | Jogging | 1.446046545267e12 | -0.344284 | 0.920286 | -1.11189 |
| user_005 | Jogging | 1.446046545291e12 | -1.41725 | -2.10702 | -4.5224 |
| user_005 | Jogging | 1.44604654538e12 | 2.91294 | -12.03198 | 4.75112 |
| user_005 | Jogging | 1.446046545453e12 | -9.04299 | -19.58108 | 1.34061 |
| user_005 | Jogging | 1.446046545541e12 | -0.919089 | -7.08866 | -5.13552 |
| user_005 | Jogging | 1.446046545582e12 | -8.65979 | -1.49389 | -8.77595 |
| user_005 | Jogging | 1.446046545598e12 | -7.77842 | -2.75846 | -4.82896 |
| user_005 | Jogging | 1.446046545663e12 | 2.52974 | -4.48288 | 2.14534 |
| user_005 | Jogging | 1.446046545785e12 | -1.95374 | -19.58108 | -10.76861 |
| user_005 | Jogging | 1.446046545841e12 | 1.07357 | 1.03525 | 4.138 |
| user_005 | Jogging | 1.446046545873e12 | 4.94392 | 17.09146 | 8.88971 |
| user_005 | Jogging | 1.446046545898e12 | 5.99e-4 | 10.15548 | 8.04667 |
| user_005 | Jogging | 1.446046545922e12 | 0.537083 | 3.83263 | 4.02303 |
| user_005 | Jogging | 1.446046546173e12 | -1.18733 | -19.58108 | -8.81427 |
| user_005 | Jogging | 1.446046546181e12 | 7.7239e-2 | -19.4278 | -14.48568 |
| user_005 | Jogging | 1.446046546359e12 | 1.76333 | -3.83143 | 1.57053 |
| user_005 | Jogging | 1.446046546376e12 | 0.460442 | -8.69811 | 2.18366 |
| user_005 | Jogging | 1.446046546424e12 | 14.29407 | -19.58108 | -5.74865 |
| user_005 | Jogging | 1.446046546481e12 | -1.64717 | -19.58108 | -7.77962 |
| user_005 | Jogging | 1.446046546505e12 | -10.1926 | -19.58108 | -4.67568 |
| user_005 | Jogging | 1.446046546529e12 | -3.79311 | -8.08499 | 4.5212 |
| user_005 | Jogging | 1.446046546577e12 | 2.98958 | 17.85786 | 11.99366 |
| user_005 | Jogging | 1.446046546627e12 | 0.15388 | 3.56439 | 4.138 |
| user_005 | Jogging | 1.446046546691e12 | 0.1922 | -19.58108 | -19.50564 |
| user_005 | Jogging | 1.44604654678e12 | -2.8351 | -10.46085 | 12.14694 |
| user_005 | Jogging | 1.446046546812e12 | -0.420925 | -15.94065 | 7.7401 |
| user_005 | Jogging | 1.44604654691e12 | 3.56439 | -10.23092 | -7.24314 |
| user_005 | Jogging | 1.446046546958e12 | -5.47921 | -0.267643 | -10.11716 |
| user_005 | Jogging | 1.446046546975e12 | -15.17425 | -2.18366 | -9.08251 |
| user_005 | Jogging | 1.446046546999e12 | -9.8094 | -2.2603 | -2.22318 |
| user_005 | Jogging | 1.446046547031e12 | 1.68669 | -3.14167 | -3.41111 |
| user_005 | Jogging | 1.446046547759e12 | -2.0687 | -2.79678 | -3.67935 |
| user_005 | Jogging | 1.446046548278e12 | -2.10702 | -18.58475 | 2.37526 |
| user_005 | Jogging | 1.446046549313e12 | 7.43474 | -19.58108 | -4.40743 |
| user_005 | Jogging | 1.446046549508e12 | -0.842448 | -4.75112 | -5.02056 |
| user_006 | Standing | 1.446046929363e12 | -4.21464 | -8.58315 | -1.03525 |
| user_006 | Standing | 1.446046929945e12 | -4.29128 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046930147e12 | -4.17632 | -8.81307 | -0.843646 |
| user_006 | Standing | 1.446046930665e12 | -4.138 | -8.73643 | -1.07357 |
| user_006 | Standing | 1.446046930722e12 | -4.3296 | -8.62147 | -0.767005 |
| user_006 | Standing | 1.446046930746e12 | -4.3296 | -8.58315 | -0.920286 |
| user_006 | Standing | 1.446046930754e12 | -4.138 | -8.54483 | -0.996927 |
| user_006 | Standing | 1.446046930867e12 | -4.138 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046930891e12 | -4.138 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046930964e12 | -4.138 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.44604693098e12 | -4.21464 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.446046931061e12 | -4.09967 | -8.46819 | -0.920286 |
| user_006 | Standing | 1.446046931094e12 | -4.138 | -8.50651 | -0.996927 |
| user_006 | Standing | 1.446046931183e12 | -4.29128 | -8.81307 | -0.881966 |
| user_006 | Standing | 1.446046931296e12 | -4.09967 | -8.69811 | -0.843646 |
| user_006 | Standing | 1.446046931361e12 | -4.17632 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046931474e12 | -4.25296 | -8.73643 | -0.996927 |
| user_006 | Standing | 1.446046931531e12 | -4.17632 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.446046931684e12 | -3.98471 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046931692e12 | -4.06135 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046931701e12 | -4.138 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046931709e12 | -4.06135 | -8.65979 | -0.805325 |
| user_006 | Standing | 1.446046931742e12 | -4.17632 | -8.69811 | -0.881966 |
| user_006 | Standing | 1.446046931782e12 | -4.17632 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.446046931894e12 | -4.25296 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046932113e12 | -4.17632 | -8.58315 | -0.843646 |
| user_006 | Standing | 1.446046932138e12 | -4.09967 | -8.73643 | -0.958607 |
| user_006 | Standing | 1.446046932153e12 | -4.25296 | -8.54483 | -0.881966 |
| user_006 | Standing | 1.446046932307e12 | -4.138 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046932389e12 | -4.17632 | -8.58315 | -0.958607 |
| user_006 | Standing | 1.446046932567e12 | -4.21464 | -8.46819 | -0.996927 |
| user_006 | Standing | 1.446046932591e12 | -4.40624 | -8.58315 | -0.881966 |
| user_006 | Standing | 1.44604693264e12 | -4.17632 | -8.77475 | -0.843646 |
| user_006 | Standing | 1.446046932648e12 | -4.17632 | -8.77475 | -0.958607 |
| user_006 | Standing | 1.446046932672e12 | -4.138 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.446046932704e12 | -4.17632 | -8.81307 | -0.996927 |
| user_006 | Standing | 1.446046932777e12 | -4.25296 | -8.50651 | -0.996927 |
| user_006 | Standing | 1.446046932785e12 | -4.29128 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.44604693281e12 | -4.21464 | -8.54483 | -0.728685 |
| user_006 | Standing | 1.446046932923e12 | -4.25296 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.44604693298e12 | -4.21464 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.446046933036e12 | -4.138 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046933044e12 | -4.29128 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046933052e12 | -4.25296 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.44604693306e12 | -4.138 | -8.58315 | -1.15021 |
| user_006 | Standing | 1.446046933157e12 | -4.25296 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.44604693323e12 | -4.17632 | -8.73643 | -0.958607 |
| user_006 | Standing | 1.446046933279e12 | -4.25296 | -8.77475 | -0.996927 |
| user_006 | Standing | 1.446046933311e12 | -4.25296 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046933344e12 | -4.25296 | -8.65979 | -0.843646 |
| user_006 | Standing | 1.446046933408e12 | -4.138 | -8.77475 | -0.843646 |
| user_006 | Standing | 1.446046934185e12 | -4.17632 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046935156e12 | -4.21464 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046937163e12 | -4.21464 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046937618e12 | -4.138 | -8.65979 | -0.958607 |
| user_006 | Standing | 1.446046938394e12 | -4.21464 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.44604693857e12 | -4.25296 | -8.62147 | -1.11189 |
| user_006 | Standing | 1.446046938732e12 | -4.5212 | -8.85139 | -1.11189 |
| user_006 | Standing | 1.446046938748e12 | -4.138 | -8.65979 | -0.958607 |
| user_006 | Standing | 1.446046938942e12 | -4.25296 | -8.50651 | -1.11189 |
| user_006 | Standing | 1.446046939023e12 | -4.21464 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046939088e12 | -4.17632 | -8.54483 | -0.881966 |
| user_006 | Standing | 1.44604693912e12 | -4.25296 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046939266e12 | -4.21464 | -8.62147 | -1.11189 |
| user_006 | Standing | 1.446046939371e12 | -4.17632 | -8.62147 | -1.07357 |
| user_006 | Standing | 1.446046939379e12 | -4.138 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.446046939436e12 | -4.36792 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.446046939485e12 | -4.21464 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046939524e12 | -4.36792 | -8.58315 | -1.03525 |
| user_006 | Standing | 1.446046939581e12 | -4.21464 | -8.54483 | -1.22685 |
| user_006 | Standing | 1.446046939589e12 | -4.3296 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046939695e12 | -4.21464 | -8.50651 | -0.996927 |
| user_006 | Standing | 1.446046939767e12 | -4.29128 | -8.69811 | -1.22685 |
| user_006 | Standing | 1.446046939776e12 | -4.29128 | -8.58315 | -1.11189 |
| user_006 | Standing | 1.446046939873e12 | -4.17632 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.446046939905e12 | -4.138 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046939938e12 | -4.36792 | -8.46819 | -0.920286 |
| user_006 | Standing | 1.446046939986e12 | -4.29128 | -8.69811 | -1.22685 |
| user_006 | Standing | 1.446046940075e12 | -4.17632 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046940132e12 | -4.29128 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046940205e12 | -4.21464 | -8.69811 | -1.07357 |
| user_006 | Standing | 1.446046940212e12 | -4.29128 | -8.73643 | -1.03525 |
| user_006 | Standing | 1.446046940253e12 | -4.25296 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.44604694031e12 | -4.21464 | -8.50651 | -0.920286 |
| user_006 | Standing | 1.446046940366e12 | -4.25296 | -8.54483 | -1.11189 |
| user_006 | Standing | 1.446046940423e12 | -4.21464 | -8.50651 | -1.03525 |
| user_006 | Standing | 1.446046940496e12 | -4.138 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046940504e12 | -4.21464 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046940552e12 | -4.21464 | -8.54483 | -1.18853 |
| user_006 | Standing | 1.446046940835e12 | -3.94639 | -8.50651 | -1.15021 |
| user_006 | Standing | 1.446046940851e12 | -4.21464 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046940997e12 | -4.21464 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046941005e12 | -4.40624 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046941102e12 | -4.40624 | -8.54483 | -1.22685 |
| user_006 | Standing | 1.446046941135e12 | -4.138 | -8.42987 | -1.03525 |
| user_006 | Standing | 1.446046941248e12 | -4.02303 | -8.69811 | -1.11189 |
| user_006 | Standing | 1.446046941288e12 | -4.25296 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.446046941361e12 | -4.06135 | -8.73643 | -0.958607 |
| user_006 | Standing | 1.446046941426e12 | -4.21464 | -8.58315 | -0.920286 |
| user_006 | Standing | 1.446046941482e12 | -4.25296 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046941531e12 | -4.21464 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.446046941547e12 | -4.3296 | -8.65979 | -1.18853 |
| user_006 | Standing | 1.446046941619e12 | -4.21464 | -8.62147 | -1.18853 |
| user_006 | Standing | 1.446046941628e12 | -4.3296 | -8.54483 | -1.18853 |
| user_006 | Standing | 1.446046941693e12 | -4.3296 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046942016e12 | -4.25296 | -8.58315 | -1.11189 |
| user_006 | Standing | 1.446046942081e12 | -4.17632 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.44604694234e12 | -4.21464 | -8.65979 | -1.07357 |
| user_006 | Standing | 1.44604694506e12 | -4.138 | -8.62147 | -0.920286 |
| user_002 | Standing | 1.446034733761e12 | -1.45557 | -9.4262 | -5.99e-4 |
| user_002 | Standing | 1.446034734537e12 | -1.37893 | -9.46452 | 0.152682 |
| user_002 | Standing | 1.446034736155e12 | -1.26397 | -9.46452 | 0.191003 |
| user_002 | Standing | 1.446034736285e12 | -1.37893 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446034736479e12 | -1.34061 | -9.46452 | 3.7722e-2 |
| user_002 | Standing | 1.44603473771e12 | -1.37893 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034739717e12 | -1.34061 | -9.38788 | 0.152682 |
| user_002 | Standing | 1.446034740817e12 | -1.49389 | -9.4262 | 0.229323 |
| user_002 | Standing | 1.446034741012e12 | -1.30229 | -9.4262 | 0.152682 |
| user_002 | Standing | 1.446034741142e12 | -1.37893 | -9.38788 | 0.305964 |
| user_002 | Standing | 1.446034741854e12 | -1.34061 | -9.46452 | 0.229323 |
| user_002 | Standing | 1.446034742075e12 | -1.41725 | -9.34956 | 0.420925 |
| user_002 | Standing | 1.446034742123e12 | -1.37893 | -9.46452 | 3.7722e-2 |
| user_002 | Standing | 1.446034742148e12 | -1.26397 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446034742252e12 | -1.37893 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446034742261e12 | -1.41725 | -9.38788 | 0.344284 |
| user_002 | Standing | 1.446034742326e12 | -1.41725 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034742367e12 | -1.34061 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.446034742455e12 | -1.26397 | -9.50284 | 0.420925 |
| user_002 | Standing | 1.446034742625e12 | -1.26397 | -9.46452 | 7.6042e-2 |
| user_002 | Standing | 1.446034742641e12 | -1.26397 | -9.50284 | 3.7722e-2 |
| user_002 | Standing | 1.446034742706e12 | -1.34061 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446034742932e12 | -0.995729 | -9.34956 | 0.344284 |
| user_002 | Standing | 1.446034742948e12 | -1.14901 | -9.4262 | 7.6042e-2 |
| user_002 | Standing | 1.446034743029e12 | -1.30229 | -9.50284 | 0.152682 |
| user_002 | Standing | 1.446034743054e12 | -1.34061 | -9.65612 | 7.6042e-2 |
| user_002 | Standing | 1.446034743159e12 | -1.34061 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034743176e12 | -1.26397 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446034743224e12 | -1.26397 | -9.69444 | 0.191003 |
| user_002 | Standing | 1.446034743345e12 | -1.18733 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446034743402e12 | -1.30229 | -9.50284 | 0.191003 |
| user_002 | Standing | 1.446034743459e12 | -1.18733 | -9.46452 | 0.152682 |
| user_002 | Standing | 1.446034743483e12 | -1.26397 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034743523e12 | -1.30229 | -9.57948 | 0.382604 |
| user_002 | Standing | 1.446034743556e12 | -1.26397 | -9.65612 | 0.267643 |
| user_002 | Standing | 1.446034743596e12 | -1.18733 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034743645e12 | -1.41725 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034743774e12 | -1.26397 | -9.46452 | 7.6042e-2 |
| user_002 | Standing | 1.446034743839e12 | -1.45557 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446034743863e12 | -1.26397 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034743888e12 | -1.14901 | -9.46452 | 0.191003 |
| user_002 | Standing | 1.446034743896e12 | -1.18733 | -9.31124 | 0.305964 |
| user_002 | Standing | 1.44603474392e12 | -1.14901 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446034744139e12 | -1.07237 | -9.34956 | 0.305964 |
| user_002 | Standing | 1.446034744146e12 | -1.34061 | -9.4262 | 0.152682 |
| user_002 | Standing | 1.446034744179e12 | -1.11069 | -9.50284 | 0.229323 |
| user_002 | Standing | 1.446034744219e12 | -1.26397 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.44603474426e12 | -1.26397 | -9.50284 | 0.229323 |
| user_002 | Standing | 1.4460347443e12 | -1.30229 | -9.54116 | 0.191003 |
| user_002 | Standing | 1.44603474447e12 | -1.14901 | -9.46452 | 0.229323 |
| user_002 | Standing | 1.446034744495e12 | -1.26397 | -9.54116 | 0.382604 |
| user_002 | Standing | 1.446034745191e12 | -0.880768 | -9.46452 | 0.344284 |
| user_002 | Standing | 1.446034745231e12 | -1.26397 | -9.27292 | 0.305964 |
| user_002 | Standing | 1.446034745288e12 | -1.30229 | -9.54116 | 0.267643 |
| user_002 | Standing | 1.446034745514e12 | -1.18733 | -9.50284 | 0.229323 |
| user_002 | Standing | 1.446034745636e12 | -1.30229 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446034745685e12 | -1.45557 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034745741e12 | -1.18733 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.44603474583e12 | -1.34061 | -9.50284 | 0.305964 |
| user_002 | Standing | 1.446034745952e12 | -1.30229 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034746017e12 | -1.30229 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446034746211e12 | -1.22565 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446034746599e12 | -1.37893 | -9.4262 | 0.229323 |
| user_002 | Standing | 1.446034746923e12 | -1.26397 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446034747053e12 | -1.22565 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446034747377e12 | -1.22565 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034748542e12 | -1.22565 | -9.38788 | 0.305964 |
| user_002 | Standing | 1.4460347488e12 | -1.22565 | -9.46452 | 0.229323 |
| user_002 | Standing | 1.446146630131e12 | -4.09967 | -8.73643 | -0.958607 |
| user_002 | Standing | 1.446146630907e12 | -4.06135 | -8.65979 | -1.03525 |
| user_002 | Standing | 1.446146631685e12 | -4.138 | -8.39155 | -1.03525 |
| user_002 | Standing | 1.446146631879e12 | -3.94639 | -8.69811 | -0.958607 |
| user_002 | Standing | 1.446146632785e12 | -6.05401 | -7.51018 | -1.83997 |
| user_002 | Standing | 1.44614663298e12 | -4.9044 | -7.97003 | -0.958607 |
| user_002 | Standing | 1.446146635505e12 | -1.49389 | -9.11964 | 2.41358 |
| user_002 | Standing | 1.446146636023e12 | -1.45557 | -9.15796 | 1.80046 |
| user_002 | Standing | 1.44614663667e12 | -1.45557 | -9.34956 | 1.68549 |
| user_002 | Standing | 1.446146637964e12 | -0.612526 | -10.23092 | 1.8771 |
| user_002 | Standing | 1.44614663803e12 | -0.382604 | -10.11596 | 1.57053 |
| user_002 | Standing | 1.446146638677e12 | 0.230521 | -9.6178 | 1.34061 |
| user_002 | Standing | 1.446146639194e12 | -0.689167 | -9.08132 | 1.64717 |
| user_002 | Standing | 1.446146639389e12 | -0.152682 | -9.27292 | 1.80046 |
| user_002 | Standing | 1.446146639907e12 | -0.229323 | -9.19628 | 1.8771 |
| user_002 | Standing | 1.446146640425e12 | -7.6042e-2 | -9.15796 | 2.03038 |
| user_002 | Standing | 1.446146641007e12 | -7.6042e-2 | -9.19628 | 2.14534 |
| user_002 | Standing | 1.446146641396e12 | -0.229323 | -9.2346 | 2.0687 |
| user_002 | Standing | 1.446146641913e12 | -0.114362 | -9.15796 | 2.22198 |
| user_002 | Standing | 1.446146642561e12 | -0.152682 | -9.11964 | 2.29862 |
| user_002 | Standing | 1.446146643337e12 | 2.18486 | -8.50651 | 1.76214 |
| user_002 | Standing | 1.446146643402e12 | 2.72134 | -8.58315 | 2.2603 |
| user_002 | Standing | 1.446146643596e12 | 3.41111 | -8.42987 | 2.2603 |
| user_002 | Standing | 1.44614664392e12 | 3.60271 | -8.23827 | 2.2603 |
| user_002 | Standing | 1.446146644308e12 | 3.79431 | -8.35323 | 2.52854 |
| user_002 | Standing | 1.446146645991e12 | 3.79431 | -8.54483 | 1.26397 |
| user_002 | Walking | 1.446034820457e12 | -4.5212 | -6.70546 | 1.83878 |
| user_002 | Walking | 1.446034822981e12 | -5.01936 | -7.39522 | 1.37893 |
| user_002 | Walking | 1.446034823565e12 | -2.91174 | -8.16163 | 2.0687 |
| user_002 | Walking | 1.446034824536e12 | -9.84772 | -7.7401 | 1.37893 |
| user_002 | Walking | 1.446034825702e12 | -6.36057 | -6.70546 | -4.5224 |
| user_002 | Walking | 1.446034826285e12 | -3.25663 | -8.23827 | 2.29862 |
| user_002 | Walking | 1.446034826932e12 | -9.27292 | -6.24561 | -3.44943 |
| user_002 | Walking | 1.446034827969e12 | -2.33694 | -6.43721 | 0.267643 |
| user_002 | Walking | 1.446034828098e12 | -4.44456 | -6.20729 | -1.76333 |
| user_002 | Walking | 1.446034828293e12 | -5.05768 | -9.08132 | 1.37893 |
| user_002 | Walking | 1.446034828811e12 | -5.90073 | -7.7401 | 2.33694 |
| user_002 | Walking | 1.446034829394e12 | -11.11229 | -6.89706 | -2.8363 |
| user_002 | Walking | 1.446034829458e12 | -7.77842 | -11.53382 | -3.10454 |
| user_002 | Walking | 1.446034829718e12 | -3.71647 | -8.23827 | 2.0687 |
| user_002 | Walking | 1.446034830106e12 | -6.55217 | -9.96268 | 2.10702 |
| user_002 | Walking | 1.446034830884e12 | -3.02671 | -7.81675 | 1.80046 |
| user_002 | Walking | 1.446034831142e12 | -4.5212 | -8.19995 | 1.57053 |
| user_002 | Walking | 1.446034831919e12 | -9.77108 | -11.07397 | -2.68302 |
| user_002 | Walking | 1.446034832309e12 | -4.29128 | -8.16163 | 1.76214 |
| user_002 | Walking | 1.446034832632e12 | -5.13432 | -10.34589 | 0.880768 |
| user_002 | Walking | 1.446034832697e12 | -2.79678 | -12.33854 | 1.76214 |
| user_002 | Walking | 1.446034833085e12 | -9.15796 | -5.82409 | -2.98958 |
| user_002 | Walking | 1.446034833539e12 | -4.86608 | -7.93171 | 1.80046 |
| user_002 | Walking | 1.446034833928e12 | -2.68182 | -11.80206 | 1.64717 |
| user_002 | Walking | 1.446034834057e12 | -5.21096 | -8.12331 | -0.460442 |
| user_002 | Walking | 1.446034835416e12 | -3.25663 | -6.16897 | -5.99e-4 |
| user_005 | Walking | 1.446046500837e12 | 2.52974 | -9.34956 | -6.01689 |
| user_005 | Walking | 1.446046500902e12 | 1.64837 | -13.64143 | 2.37526 |
| user_005 | Walking | 1.44604650129e12 | -3.40991 | -10.80573 | -0.537083 |
| user_005 | Walking | 1.446046501485e12 | 0.575403 | -11.76374 | 0.842448 |
| user_005 | Walking | 1.446046502779e12 | -1.53221 | -2.22198 | 0.804128 |
| user_005 | Walking | 1.446046502844e12 | 1.11189 | -1.22565 | -1.22685 |
| user_005 | Walking | 1.44604650375e12 | 0.1922 | -9.08132 | 7.6042e-2 |
| user_005 | Walking | 1.446046503815e12 | -2.91174 | -4.75112 | 0.267643 |
| user_005 | Walking | 1.446046503945e12 | 1.03525 | -4.21464 | -4.25415 |
| user_005 | Walking | 1.446046504592e12 | -3.83143 | -11.8787 | -0.767005 |
| user_005 | Walking | 1.446046504916e12 | -1.99206 | -3.52487 | 0.804128 |
| user_005 | Walking | 1.446046506534e12 | 0.652044 | -7.08866 | -0.920286 |
| user_005 | Walking | 1.446046506858e12 | -0.420925 | -10.80573 | 0.765807 |
| user_005 | Walking | 1.446046506922e12 | 0.728685 | -13.14327 | 0.995729 |
| user_005 | Walking | 1.446046507182e12 | 0.537083 | -1.03405 | 7.6042e-2 |
| user_005 | Walking | 1.446046507764e12 | -1.03405 | -8.23827 | -0.958607 |
| user_005 | Walking | 1.446046508152e12 | 2.18486 | -10.84405 | -5.99e-4 |
| user_005 | Walking | 1.446046508411e12 | 1.53341 | -13.64143 | -10.61533 |
| user_005 | Walking | 1.446046508928e12 | -2.29862 | -9.65612 | -0.805325 |
| user_005 | Walking | 1.446046508994e12 | -2.10702 | -12.0703 | 0.305964 |
| user_005 | Walking | 1.446046509123e12 | 1.34181 | -11.15061 | 0.344284 |
| user_005 | Walking | 1.446046509512e12 | 1.45677 | -8.50651 | -6.85994 |
| user_005 | Walking | 1.446046509641e12 | 3.60271 | -8.39155 | -3.94759 |
| user_005 | Walking | 1.446046511324e12 | -3.37159 | -12.30022 | 0.344284 |
| user_005 | Walking | 1.446046512036e12 | 3.0279 | -6.62882 | 2.29862 |
| user_005 | Walking | 1.446046513265e12 | -2.8351 | -12.60678 | -0.15388 |
| user_005 | Walking | 1.446046513783e12 | 3.71767 | -15.36585 | 0.535886 |
| user_005 | Walking | 1.446046514042e12 | 1.15021 | -3.33327 | -2.98958 |
| user_005 | Walking | 1.446046514624e12 | -3.7722e-2 | -9.19628 | -1.30349 |
| user_005 | Walking | 1.446046515789e12 | -2.03038 | -10.84405 | -0.958607 |
| user_002 | Jogging | 1.446034899372e12 | -16.05561 | -15.28921 | -7.7239e-2 |
| user_002 | Jogging | 1.446034899502e12 | -5.17264 | -2.29862 | -0.652044 |
| user_002 | Jogging | 1.446034900279e12 | -4.67448 | -1.30229 | -0.498763 |
| user_002 | Jogging | 1.446034901309e12 | -8.96635 | -3.14167 | -6.47673 |
| user_002 | Jogging | 1.446034901349e12 | -5.74745 | -2.95007 | 2.22198 |
| user_002 | Jogging | 1.446034901454e12 | 4.94392 | -0.344284 | -2.60638 |
| user_002 | Jogging | 1.446034901495e12 | -0.535886 | -4.06135 | 0.305964 |
| user_002 | Jogging | 1.446034901503e12 | -3.40991 | -3.48655 | 2.8351 |
| user_002 | Jogging | 1.44603490156e12 | -9.6178 | -14.02463 | 3.86975 |
| user_002 | Jogging | 1.446034901592e12 | -17.20522 | -15.32753 | 7.62514 |
| user_002 | Jogging | 1.446034901616e12 | -13.79471 | -16.24721 | -0.307161 |
| user_002 | Jogging | 1.446034901649e12 | -15.13592 | -15.97897 | 2.18366 |
| user_002 | Jogging | 1.446034901843e12 | -1.76214 | -1.80046 | 0.804128 |
| user_002 | Jogging | 1.446034901932e12 | -1.22565 | -9.69444 | -3.87095 |
| user_002 | Jogging | 1.446034901956e12 | -15.48081 | -16.01729 | -5.71033 |
| user_002 | Jogging | 1.446034901964e12 | -18.66139 | -12.53014 | -3.44943 |
| user_002 | Jogging | 1.446034902207e12 | 1.57173 | 1.22685 | -1.15021 |
| user_002 | Jogging | 1.446034902288e12 | -0.995729 | -4.86608 | -2.4531 |
| user_002 | Jogging | 1.446034902353e12 | -14.5228 | -8.69811 | 3.21831 |
| user_002 | Jogging | 1.446034902458e12 | -19.58108 | -12.91335 | -2.87462 |
| user_002 | Jogging | 1.446034902483e12 | -13.25823 | -9.96268 | -2.87462 |
| user_002 | Jogging | 1.446034902531e12 | -2.33694 | -4.55952 | -1.61005 |
| user_002 | Jogging | 1.446034902782e12 | -15.82569 | -12.79839 | -9.19747 |
| user_002 | Jogging | 1.446034902798e12 | -18.23987 | -10.72909 | -7.20482 |
| user_002 | Jogging | 1.446034902951e12 | 3.87095 | 1.87829 | -3.0279 |
| user_002 | Jogging | 1.446034903016e12 | 1.72501 | -3.75479 | -1.61005 |
| user_002 | Jogging | 1.446034903041e12 | -4.02303 | -5.40257 | -3.8919e-2 |
| user_002 | Jogging | 1.44603490321e12 | -14.40784 | -10.61413 | -0.1922 |
| user_002 | Jogging | 1.446034903227e12 | -11.57214 | -9.69444 | -1.64837 |
| user_002 | Jogging | 1.446034903251e12 | -7.1653 | -7.20362 | -2.0699 |
| user_002 | Jogging | 1.446034903494e12 | -11.41886 | -17.12858 | -6.01689 |
| user_002 | Jogging | 1.446034903526e12 | -16.89866 | -17.74171 | -7.97122 |
| user_002 | Jogging | 1.446034903704e12 | 2.75966 | 2.18486 | -1.49509 |
| user_002 | Jogging | 1.446034903891e12 | -14.79104 | -15.78737 | 2.56686 |
| user_002 | Jogging | 1.446034903955e12 | -11.30389 | -11.76374 | 1.80046 |
| user_002 | Jogging | 1.446034904197e12 | -0.995729 | -3.33327 | -0.1922 |
| user_002 | Jogging | 1.446034904238e12 | -10.57581 | -14.21624 | -4.5224 |
| user_002 | Jogging | 1.446034904352e12 | -17.70339 | -6.13065 | -7.77962 |
| user_002 | Jogging | 1.446034904376e12 | -10.95901 | -5.13432 | -5.82529 |
| user_002 | Jogging | 1.446034904408e12 | -7.05034 | -2.91174 | -1.49509 |
| user_002 | Jogging | 1.446034904457e12 | -0.497565 | -0.382604 | 2.22198 |
| user_002 | Jogging | 1.446034904692e12 | -12.6451 | -12.30022 | -0.920286 |
| user_002 | Jogging | 1.446034904854e12 | -5.36425 | 3.14286 | -2.52974 |
| user_002 | Jogging | 1.44603490491e12 | -1.34061 | -1.11069 | 0.497565 |
| user_002 | Jogging | 1.44603490508e12 | -17.24354 | -10.95901 | -8.69931 |
| user_002 | Jogging | 1.446034905194e12 | -1.60885 | 1.11189 | 2.91174 |
| user_002 | Jogging | 1.446034905291e12 | -2.4519 | -4.44456 | -2.98958 |
| user_002 | Jogging | 1.446034905314e12 | -5.93905 | -4.138 | 1.22565 |
| user_002 | Jogging | 1.446034905542e12 | -10.57581 | -6.47553 | -3.18118 |
| user_002 | Jogging | 1.446034905671e12 | -2.98839 | -2.95007 | -0.881966 |
| user_002 | Jogging | 1.446034905711e12 | -1.37893 | -3.29495 | 4.02303 |
| user_002 | Jogging | 1.446034905759e12 | -11.80206 | -13.64143 | -5.59536 |
| user_002 | Jogging | 1.446034905963e12 | 1.99325 | 1.11189 | 2.79678 |
| user_002 | Jogging | 1.446034906011e12 | 5.36544 | -0.114362 | -0.307161 |
| user_002 | Jogging | 1.446034906051e12 | 0.996927 | -6.47553 | -3.41111 |
| user_002 | Jogging | 1.446034906068e12 | -7.12698 | -5.47921 | -0.498763 |
| user_002 | Jogging | 1.446034906092e12 | -9.69444 | -6.59049 | -4.48408 |
| user_002 | Jogging | 1.446034906132e12 | -15.74905 | -17.81835 | 0.344284 |
| user_002 | Jogging | 1.446034906189e12 | -15.44249 | -13.75639 | -2.03158 |
| user_002 | Jogging | 1.446034906254e12 | -10.49917 | -11.99366 | -1.80165 |
| user_002 | Jogging | 1.446034906278e12 | -7.47186 | -9.2346 | -2.37646 |
| user_002 | Jogging | 1.446034906391e12 | -3.10335 | 1.64837 | -4.82896 |
| user_002 | Jogging | 1.446034906456e12 | 0.230521 | -2.49022 | 2.10702 |
| user_002 | Jogging | 1.446034906529e12 | -17.12858 | -10.46085 | -8.27779 |
| user_002 | Jogging | 1.446034906601e12 | -19.35116 | -4.138 | -7.62634 |
| user_002 | Jogging | 1.446034906853e12 | -8.96635 | -3.06503 | -1.95493 |
| user_002 | Jogging | 1.446034906861e12 | -9.34956 | -4.25296 | -3.0279 |
| user_002 | Jogging | 1.446034906885e12 | -10.76741 | -14.98264 | -1.15021 |
| user_002 | Jogging | 1.446034906917e12 | -13.64143 | -11.8787 | 3.21831 |
| user_002 | Jogging | 1.446034906925e12 | -14.10128 | -10.07764 | 4.3296 |
| user_002 | Jogging | 1.446034907022e12 | -13.67975 | -11.61046 | -2.52974 |
| user_002 | Jogging | 1.446034907055e12 | -8.96635 | -6.28393 | -2.37646 |
| user_002 | Jogging | 1.446034907095e12 | -2.98839 | -2.75846 | -1.83997 |
| user_002 | Jogging | 1.446034907136e12 | -3.75479 | 4.5224 | -1.34181 |
| user_002 | Jogging | 1.446034907193e12 | -2.79678 | -1.18733 | -0.537083 |
| user_002 | Jogging | 1.446034907355e12 | -19.58108 | -9.31124 | -9.69564 |
| user_002 | Jogging | 1.446034907386e12 | -18.00995 | -4.82776 | -7.01322 |
| user_002 | Jogging | 1.446034907443e12 | -8.77475 | -1.57053 | -2.6447 |
| user_002 | Jogging | 1.446034907484e12 | 5.99e-4 | -1.80046 | 0.957409 |
| user_002 | Jogging | 1.446034907605e12 | -4.9044 | -1.57053 | -0.11556 |
| user_002 | Jogging | 1.446034907637e12 | -8.42987 | -1.18733 | -0.728685 |
| user_002 | Jogging | 1.446034907702e12 | -18.20155 | -7.01202 | 5.36425 |
| user_002 | Jogging | 1.446034907824e12 | -12.72175 | -6.70546 | -3.33447 |
| user_002 | Jogging | 1.446034907921e12 | -2.98839 | 4.59904 | -2.68302 |
| user_002 | Jogging | 1.446034908139e12 | -16.20889 | -13.83303 | -8.43107 |
| user_002 | Jogging | 1.446034908528e12 | -15.48081 | -12.72175 | 0.765807 |
| user_002 | Jogging | 1.446034910081e12 | -9.73276 | -9.50284 | -1.11189 |
| user_002 | Jogging | 1.446034910146e12 | -13.60311 | -11.11229 | 0.459245 |
| user_002 | Jogging | 1.446034910664e12 | -8.27659 | -1.68549 | -1.11189 |
| user_002 | Jogging | 1.44603491183e12 | -10.76741 | -8.62147 | -2.72134 |
| user_002 | Jogging | 1.446034912283e12 | 0.268841 | -0.191003 | 1.80046 |
| user_002 | Jogging | 1.446034912348e12 | 4.714 | -1.03405 | -0.652044 |
| user_002 | Jogging | 1.446034912671e12 | -6.13065 | -0.420925 | -3.10454 |
| user_002 | Jogging | 1.446034914744e12 | -4.94272 | -6.16897 | 0.995729 |
| user_002 | Jogging | 1.446034915261e12 | -13.02831 | -10.76741 | -6.51505 |
| user_005 | Jumping | 1.446046629982e12 | 0.15388 | -0.191003 | -1.91661 |
| user_005 | Jumping | 1.446046630436e12 | 1.30349 | -4.09967 | -4.82896 |
| user_005 | Jumping | 1.44604663063e12 | 0.958607 | 0.805325 | 0.420925 |
| user_005 | Jumping | 1.44604663296e12 | 7.47306 | -19.58108 | -5.78697 |
| user_005 | Jumping | 1.446046634968e12 | 1.64837 | -16.70706 | -0.996927 |
| user_005 | Jumping | 1.446046635551e12 | 14.14079 | -19.58108 | -9.00587 |
| user_005 | Jumping | 1.446046636393e12 | -0.267643 | 0.460442 | -3.71767 |
| user_005 | Jumping | 1.446046637105e12 | -1.95374 | 2.10822 | 3.79311 |
| user_005 | Jumping | 1.446046637622e12 | -0.382604 | -5.74745 | -0.575403 |
| user_005 | Jumping | 1.446046638918e12 | 0.498763 | -2.91174 | -3.10454 |
| user_005 | Jumping | 1.446046639176e12 | 7.01322 | -4.75112 | -1.03525 |
| user_005 | Jumping | 1.446046639435e12 | -0.305964 | -2.03038 | 1.8771 |
| user_005 | Jumping | 1.446046640083e12 | -2.10702 | -0.612526 | 0.420925 |
| user_005 | Jumping | 1.446046640666e12 | -1.64717 | 1.34181 | -1.11189 |
| user_005 | Jumping | 1.446046640926e12 | 7.43474 | -19.58108 | -4.9056 |
| user_005 | Jumping | 1.446046641509e12 | 7.97122 | -19.58108 | -5.74865 |
| user_005 | Jumping | 1.446046642544e12 | 0.652044 | 5.99e-4 | -1.11189 |
| user_005 | Jumping | 1.446046642609e12 | 0.383802 | 7.7239e-2 | -0.460442 |
| user_005 | Jumping | 1.446046642803e12 | 5.17384 | -19.58108 | -7.05154 |
| user_005 | Jumping | 1.446046643062e12 | -1.68549 | 0.690364 | -1.18853 |
| user_005 | Jumping | 1.446046643645e12 | -1.76214 | -0.229323 | -3.79431 |
| user_005 | Jumping | 1.446046644292e12 | -0.995729 | 0.383802 | -6.85994 |
| user_005 | Jumping | 1.446046644681e12 | 2.22318 | -11.72542 | -9.31244 |
| user_005 | Jumping | 1.446046645004e12 | 7.7239e-2 | -14.17792 | 4.36792 |
| user_005 | Jumping | 1.446046645069e12 | 7.7239e-2 | -6.7821 | -2.18486 |
| user_005 | Jumping | 1.446046645199e12 | 2.87462 | -7.6042e-2 | 0.880768 |
| user_005 | Jumping | 1.446046645263e12 | -1.95374 | -3.7722e-2 | -1.72501 |
| user_005 | Jumping | 1.446046646428e12 | -2.37526 | 1.57173 | -1.91661 |
| user_001 | Walking | 1.44604718009e12 | 2.91294 | -5.59417 | -0.537083 |
| user_001 | Walking | 1.446047180349e12 | 7.43474 | -9.34956 | -1.99325 |
| user_001 | Walking | 1.446047180543e12 | 1.80165 | -10.11596 | 0.305964 |
| user_001 | Walking | 1.446047181061e12 | 4.63736 | -8.88971 | 0.650847 |
| user_001 | Walking | 1.44604718132e12 | 2.37646 | -5.63249 | -1.34181 |
| user_001 | Walking | 1.446047181709e12 | 4.13919 | -10.1926 | 1.07237 |
| user_001 | Walking | 1.446047181967e12 | 1.80165 | -6.47553 | -5.99e-4 |
| user_001 | Walking | 1.446047182226e12 | 5.17384 | -9.19628 | 3.7722e-2 |
| user_001 | Walking | 1.44604718378e12 | 4.02423 | -7.05034 | 0.650847 |
| user_001 | Walking | 1.446047183844e12 | 5.55704 | -7.93171 | -1.07357 |
| user_001 | Walking | 1.44604718611e12 | 7.05154 | -8.04667 | -2.14654 |
| user_001 | Walking | 1.446047186887e12 | 5.99e-4 | -13.71807 | 3.56319 |
| user_001 | Walking | 1.446047187145e12 | -2.49022 | -6.24561 | -0.996927 |
| user_001 | Walking | 1.446047188246e12 | -1.60885 | -5.63249 | 0.459245 |
| user_001 | Walking | 1.44604718844e12 | 7.9329 | -12.03198 | 3.86975 |
| user_001 | Walking | 1.446047188504e12 | 2.33814 | -11.99366 | 3.60151 |
| user_001 | Walking | 1.446047190059e12 | 1.30349 | -8.58315 | 1.72382 |
| user_001 | Walking | 1.446047190511e12 | -0.804128 | -7.58682 | 0.842448 |
| user_001 | Walking | 1.446047193943e12 | 4.29247 | -9.04299 | 1.14901 |
| user_001 | Walking | 1.446047194331e12 | 3.44943 | -8.46819 | -0.767005 |
| user_001 | Walking | 1.446047194526e12 | 4.98224 | -9.11964 | -2.37646 |
| user_001 | Walking | 1.446047195173e12 | 1.95493 | -13.10495 | 2.8351 |
| user_001 | Walking | 1.446047196144e12 | 5.94025 | -9.96268 | 1.07237 |
| user_001 | Walking | 1.446047196338e12 | 2.72134 | -11.76374 | -1.95493 |
| user_002 | Jumping | 1.446035033669e12 | -8.35323 | -10.92069 | 1.95374 |
| user_002 | Jumping | 1.446035033927e12 | -0.842448 | -0.650847 | -1.26517 |
| user_002 | Jumping | 1.446035034057e12 | -6.70546 | -11.6871 | 0.842448 |
| user_002 | Jumping | 1.44603503464e12 | -9.57948 | -12.49182 | 0.880768 |
| user_002 | Jumping | 1.446035034834e12 | -7.5485 | -10.46085 | 1.30229 |
| user_002 | Jumping | 1.446035035093e12 | -1.45557 | -0.497565 | -1.07357 |
| user_002 | Jumping | 1.446035036711e12 | -0.880768 | 1.38013 | -0.575403 |
| user_002 | Jumping | 1.446035036777e12 | -0.420925 | -0.689167 | -0.652044 |
| user_002 | Jumping | 1.446035037424e12 | -1.49389 | 3.8919e-2 | -0.460442 |
| user_002 | Jumping | 1.446035038266e12 | -16.66874 | -19.58108 | -0.537083 |
| user_002 | Jumping | 1.446035039496e12 | -16.20889 | -18.85299 | -1.57173 |
| user_002 | Jumping | 1.44603504079e12 | -1.76214 | -1.57053 | -1.22685 |
| user_002 | Jumping | 1.446035042085e12 | -1.41725 | -0.842448 | -1.26517 |
| user_002 | Jumping | 1.446035042344e12 | -11.6871 | -17.85667 | -0.690364 |
| user_002 | Jumping | 1.446035042732e12 | -0.650847 | -0.152682 | 1.14901 |
| user_002 | Jumping | 1.446035043703e12 | -1.11069 | -5.78577 | -0.307161 |
| user_002 | Jumping | 1.44603504448e12 | -0.459245 | -0.229323 | -1.18853 |
| user_002 | Jumping | 1.446035044933e12 | -2.18366 | -4.25296 | 0.229323 |
| user_002 | Jumping | 1.446035045516e12 | 0.460442 | -2.2603 | 0.229323 |
| user_002 | Jumping | 1.446035046292e12 | -0.152682 | -1.34061 | -7.7239e-2 |
| user_002 | Jumping | 1.446035046745e12 | 0.575403 | 1.30349 | -1.87829 |
| user_002 | Jumping | 1.446035046875e12 | -0.842448 | -1.03405 | -0.345482 |
| user_002 | Jumping | 1.446035047263e12 | -4.06135 | -5.90073 | -3.8919e-2 |
| user_002 | Jumping | 1.446035049399e12 | -13.52647 | -17.3585 | -0.958607 |
| user_002 | Jumping | 1.446035049529e12 | -9.84772 | -15.25089 | 1.18733 |
| user_002 | Jumping | 1.446035049593e12 | -3.29495 | -3.79311 | -7.7239e-2 |
| user_003 | Sitting | 1.446047527172e12 | 0.498763 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047527754e12 | 0.498763 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047527949e12 | 0.422122 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047528466e12 | 0.460442 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.44604752879e12 | 0.460442 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.446047529632e12 | 0.498763 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.446047529672e12 | 0.383802 | -0.765807 | 9.34956 |
| user_003 | Sitting | 1.446047530142e12 | 0.575403 | -0.765807 | 9.46452 |
| user_003 | Sitting | 1.44604753015e12 | 0.268841 | -0.574206 | 9.38788 |
| user_003 | Sitting | 1.446047530231e12 | 0.460442 | -0.612526 | 9.54116 |
| user_003 | Sitting | 1.446047530287e12 | 0.268841 | -0.459245 | 9.38788 |
| user_003 | Sitting | 1.446047530304e12 | 0.383802 | -0.535886 | 9.54116 |
| user_003 | Sitting | 1.446047530481e12 | 0.383802 | -0.727487 | 9.38788 |
| user_003 | Sitting | 1.446047530498e12 | 0.537083 | -0.804128 | 9.38788 |
| user_003 | Sitting | 1.446047530659e12 | 0.383802 | -0.727487 | 9.54116 |
| user_003 | Sitting | 1.446047530806e12 | 0.498763 | -0.842448 | 9.38788 |
| user_003 | Sitting | 1.44604753083e12 | 0.498763 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047530871e12 | 0.575403 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047530927e12 | 0.460442 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047530991e12 | 0.460442 | -0.765807 | 9.46452 |
| user_003 | Sitting | 1.446047531048e12 | 0.498763 | -0.842448 | 9.34956 |
| user_003 | Sitting | 1.446047531072e12 | 0.537083 | -0.842448 | 9.50284 |
| user_003 | Sitting | 1.446047531161e12 | 0.383802 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047531169e12 | 0.537083 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047531275e12 | 0.307161 | -0.804128 | 9.4262 |
| user_003 | Sitting | 1.446047531291e12 | 0.383802 | -0.727487 | 9.34956 |
| user_003 | Sitting | 1.446047531364e12 | 0.498763 | -0.804128 | 9.50284 |
| user_003 | Sitting | 1.446047531526e12 | 0.307161 | -0.574206 | 9.46452 |
| user_003 | Sitting | 1.446047531566e12 | 0.652044 | -0.612526 | 9.50284 |
| user_003 | Sitting | 1.446047531598e12 | 0.498763 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047531809e12 | 0.498763 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047531817e12 | 0.268841 | -0.804128 | 9.27292 |
| user_003 | Sitting | 1.446047531833e12 | 0.537083 | -0.880768 | 9.38788 |
| user_003 | Sitting | 1.446047531841e12 | 0.537083 | -0.842448 | 9.4262 |
| user_003 | Sitting | 1.446047531857e12 | 0.422122 | -0.727487 | 9.6178 |
| user_003 | Sitting | 1.446047531897e12 | 0.498763 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047532003e12 | 0.575403 | -0.574206 | 9.46452 |
| user_003 | Sitting | 1.446047532051e12 | 0.613724 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047532099e12 | 0.383802 | -0.612526 | 9.50284 |
| user_003 | Sitting | 1.446047532165e12 | 0.422122 | -0.574206 | 9.54116 |
| user_003 | Sitting | 1.446047532214e12 | 0.537083 | -0.612526 | 9.54116 |
| user_003 | Sitting | 1.446047532391e12 | 0.460442 | -0.535886 | 9.4262 |
| user_003 | Sitting | 1.44604753278e12 | 0.460442 | -0.535886 | 9.34956 |
| user_003 | Sitting | 1.446047533832e12 | 0.498763 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047534285e12 | 0.498763 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047534738e12 | 0.460442 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047534803e12 | 0.422122 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047535645e12 | 0.422122 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.446047536357e12 | 0.498763 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047537005e12 | 0.498763 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047537393e12 | 0.460442 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.446047537522e12 | 0.460442 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047537963e12 | 2.91294 | -4.06135 | -17.7429 |
| user_003 | Sitting | 1.446047537979e12 | 0.537083 | -0.574206 | 9.31124 |
| user_003 | Sitting | 1.446047538011e12 | 0.575403 | -0.650847 | 9.57948 |
| user_003 | Sitting | 1.446047538044e12 | 0.422122 | -0.919089 | 9.50284 |
| user_003 | Sitting | 1.446047538084e12 | 0.422122 | -0.765807 | 9.54116 |
| user_003 | Sitting | 1.446047538093e12 | 0.575403 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.446047538246e12 | 0.690364 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.446047538279e12 | 0.383802 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047538287e12 | 0.460442 | -0.842448 | 9.54116 |
| user_003 | Sitting | 1.446047538392e12 | 0.460442 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.4460475384e12 | 0.498763 | -0.765807 | 9.34956 |
| user_003 | Sitting | 1.44604753844e12 | 0.422122 | -0.765807 | 9.50284 |
| user_003 | Sitting | 1.446047538538e12 | 0.422122 | -0.535886 | 9.38788 |
| user_003 | Sitting | 1.44604753857e12 | 0.498763 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047538586e12 | 0.345482 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.44604753865e12 | 0.460442 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047538691e12 | 0.345482 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.4460475387e12 | 0.460442 | -0.612526 | 9.57948 |
| user_003 | Sitting | 1.446047538731e12 | 0.422122 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047538861e12 | 0.422122 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047538934e12 | 0.498763 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.446047539015e12 | 0.690364 | -0.727487 | 9.54116 |
| user_003 | Sitting | 1.446047539136e12 | 0.422122 | -0.765807 | 9.38788 |
| user_003 | Sitting | 1.44604753925e12 | 0.575403 | -0.650847 | 9.54116 |
| user_003 | Sitting | 1.446047539355e12 | 0.345482 | -0.880768 | 9.4262 |
| user_003 | Sitting | 1.446047539468e12 | 0.460442 | -0.765807 | 9.34956 |
| user_003 | Sitting | 1.446047539485e12 | 0.498763 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047539574e12 | 0.383802 | -0.919089 | 9.38788 |
| user_003 | Sitting | 1.446047539622e12 | 0.383802 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.446047539711e12 | 0.422122 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047539759e12 | 0.537083 | -0.420925 | 9.4262 |
| user_003 | Sitting | 1.446047539897e12 | 0.498763 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047539995e12 | 0.422122 | -0.689167 | 9.27292 |
| user_003 | Sitting | 1.446047540107e12 | 0.460442 | -0.650847 | 9.34956 |
| user_003 | Sitting | 1.446047540131e12 | 0.652044 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047540164e12 | 0.422122 | -0.574206 | 9.50284 |
| user_003 | Sitting | 1.446047540236e12 | 0.613724 | -0.765807 | 9.4262 |
| user_003 | Sitting | 1.446047540464e12 | 0.498763 | -0.535886 | 9.31124 |
| user_003 | Sitting | 1.44604754052e12 | 0.422122 | -0.689167 | 9.65612 |
| user_003 | Sitting | 1.446047540593e12 | 0.460442 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047540706e12 | 0.537083 | -0.765807 | 9.34956 |
| user_003 | Sitting | 1.446047540884e12 | 0.498763 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047540891e12 | 0.575403 | -0.574206 | 9.54116 |
| user_003 | Sitting | 1.446047540956e12 | 0.422122 | -0.535886 | 9.38788 |
| user_003 | Sitting | 1.446047541046e12 | 0.345482 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047541127e12 | 0.460442 | -0.574206 | 9.38788 |
| user_003 | Sitting | 1.44604754124e12 | 0.307161 | -0.574206 | 9.38788 |
| user_003 | Sitting | 1.446047542219e12 | 0.383802 | -0.689167 | 9.4262 |
| user_006 | Sitting | 1.446035991203e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035993082e12 | -0.191003 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035993534e12 | -0.305964 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446035994246e12 | -0.267643 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446035994311e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035994636e12 | -0.229323 | -1.14901 | 9.4262 |
| user_006 | Sitting | 1.446035994701e12 | -0.152682 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446035996838e12 | -0.267643 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446035997161e12 | -0.191003 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.44603599742e12 | -0.229323 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446035997808e12 | -0.305964 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035997873e12 | -0.267643 | -1.18733 | 9.34956 |
| user_006 | Sitting | 1.44603599865e12 | -0.267643 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446035998715e12 | -0.152682 | -1.14901 | 9.34956 |
| user_006 | Sitting | 1.446035999622e12 | -0.267643 | -1.30229 | 9.38788 |
| user_006 | Sitting | 1.446036000982e12 | -0.229323 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.44603600286e12 | -0.229323 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446036003638e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036003702e12 | -0.305964 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.44603600571e12 | -0.191003 | -1.26397 | 9.38788 |
| user_006 | Sitting | 1.446036005904e12 | -0.267643 | -1.26397 | 9.38788 |
| user_006 | Sitting | 1.446036007069e12 | -0.267643 | -1.18733 | 9.34956 |
| user_007 | Jogging | 1.446309659174e12 | -12.60678 | -8.85139 | 2.22198 |
| user_007 | Jogging | 1.446309662866e12 | 1.38013 | -0.689167 | 2.4519 |
| user_007 | Jogging | 1.446309664291e12 | -6.47553 | -2.18366 | -0.690364 |
| user_007 | Jogging | 1.446309664485e12 | -16.32385 | -9.6178 | -2.37646 |
| user_007 | Jogging | 1.446309664551e12 | -11.84038 | -12.76007 | -1.15021 |
| user_007 | Jogging | 1.446309664939e12 | -10.57581 | -10.23092 | -5.86361 |
| user_007 | Jogging | 1.446309665133e12 | 2.95126 | -3.10335 | 1.72382 |
| user_007 | Jogging | 1.446309665263e12 | -16.17057 | -11.22725 | -2.91294 |
| user_007 | Jogging | 1.446309666558e12 | -12.91335 | -12.14694 | -5.55704 |
| user_007 | Jogging | 1.446309668307e12 | 0.230521 | -1.68549 | 1.49389 |
| user_007 | Jogging | 1.446309668695e12 | -3.25663 | -1.41725 | -2.68302 |
| user_007 | Jogging | 1.446309669538e12 | -1.49389 | -1.68549 | 0.305964 |
| user_007 | Jogging | 1.446309670574e12 | -13.948 | -0.574206 | -7.3581 |
| user_007 | Jogging | 1.446309670833e12 | -12.72175 | -11.26557 | 1.45557 |
| user_007 | Jogging | 1.446309671805e12 | -11.64878 | -5.47921 | -1.34181 |
| user_007 | Jogging | 1.446309672324e12 | -2.18366 | -1.07237 | 2.4519 |
| user_007 | Jogging | 1.446309672648e12 | -10.72909 | -6.70546 | -2.2615 |
| user_007 | Jogging | 1.446309672971e12 | -16.51545 | -10.38421 | -4.13919 |
| user_007 | Jogging | 1.446309675886e12 | -3.79311 | -7.51018 | -1.49509 |
| user_004 | Walking | 1.446046366398e12 | -3.98471 | -13.79471 | 0.459245 |
| user_004 | Walking | 1.446046366463e12 | -3.63983 | -2.14534 | 1.72382 |
| user_004 | Walking | 1.446046368406e12 | -1.11069 | -3.29495 | -4.40743 |
| user_004 | Walking | 1.446046369247e12 | -7.20362 | -13.83303 | 1.14901 |
| user_004 | Walking | 1.446046370866e12 | -4.67448 | -7.3569 | -0.422122 |
| user_004 | Walking | 1.446046371124e12 | 0.230521 | -9.08132 | 7.6042e-2 |
| user_004 | Walking | 1.446046372679e12 | -4.06135 | -9.4262 | 0.535886 |
| user_004 | Walking | 1.446046374556e12 | -0.305964 | -9.31124 | 0.229323 |
| user_004 | Walking | 1.446046376046e12 | -8.04667 | -12.30022 | 0.497565 |
| user_004 | Walking | 1.446046376175e12 | 0.498763 | -7.05034 | 0.535886 |
| user_004 | Walking | 1.446046376304e12 | -0.497565 | -3.83143 | -5.32712 |
| user_004 | Walking | 1.446046376822e12 | 0.15388 | -9.54116 | -7.7239e-2 |
| user_004 | Walking | 1.446046378118e12 | 1.80165 | -11.6871 | -1.30349 |
| user_004 | Walking | 1.446046378571e12 | 0.268841 | -5.63249 | -6.20849 |
| user_004 | Walking | 1.446046378765e12 | -4.59784 | -14.906 | 1.14901 |
| user_004 | Walking | 1.446046379154e12 | 0.575403 | -9.54116 | 0.535886 |
| user_004 | Walking | 1.446046379801e12 | 1.80165 | -10.1926 | -7.43474 |
| user_004 | Walking | 1.446046380154e12 | -0.305964 | -9.8094 | -0.613724 |
| user_004 | Walking | 1.446046380163e12 | 0.345482 | -9.84772 | -0.728685 |
| user_004 | Walking | 1.44604638022e12 | -0.842448 | -8.81307 | 0.689167 |
| user_004 | Walking | 1.446046380244e12 | -1.57053 | -8.23827 | 1.72382 |
| user_004 | Walking | 1.446046380252e12 | -0.842448 | -8.39155 | 1.34061 |
| user_004 | Walking | 1.446046380324e12 | 3.90927 | -10.84405 | 0.305964 |
| user_004 | Walking | 1.446046380446e12 | 2.49142 | -12.83671 | 1.57053 |
| user_004 | Walking | 1.446046380454e12 | 2.22318 | -13.37319 | 0.459245 |
| user_004 | Walking | 1.446046380511e12 | -8.31491 | -15.74905 | -0.383802 |
| user_004 | Walking | 1.446046380527e12 | -9.54116 | -13.83303 | 1.83878 |
| user_004 | Walking | 1.446046380632e12 | 1.91661 | -9.04299 | -1.95493 |
| user_004 | Walking | 1.446046380907e12 | 0.805325 | -13.90968 | -7.08986 |
| user_004 | Walking | 1.446046380924e12 | -1.03405 | -10.11596 | -6.43841 |
| user_004 | Walking | 1.446046380931e12 | -1.14901 | -7.62514 | -7.9329 |
| user_004 | Walking | 1.446046380948e12 | -1.95374 | -6.85874 | -10.27044 |
| user_004 | Walking | 1.446046380972e12 | -3.44823 | -16.51545 | -2.98958 |
| user_004 | Walking | 1.446046380996e12 | -6.39889 | -18.04827 | 4.5212 |
| user_004 | Walking | 1.446046381028e12 | -3.44823 | -2.75846 | -4.25415 |
| user_004 | Walking | 1.446046381068e12 | -3.40991 | -2.60518 | 3.44823 |
| user_004 | Walking | 1.446046381109e12 | -2.6435 | -10.26924 | 2.10702 |
| user_004 | Walking | 1.446046381166e12 | 4.63736 | -9.92436 | -3.29615 |
| user_004 | Walking | 1.446046381246e12 | -3.52487 | -6.24561 | 1.8771 |
| user_004 | Walking | 1.446046381255e12 | -3.02671 | -6.82042 | 1.57053 |
| user_004 | Walking | 1.446046381344e12 | -0.995729 | -9.31124 | 0.919089 |
| user_004 | Walking | 1.446046381482e12 | 0.805325 | -12.37686 | -3.0279 |
| user_004 | Walking | 1.446046381602e12 | -9.27292 | -16.51545 | -0.460442 |
| user_004 | Walking | 1.446046381708e12 | 3.41111 | -8.62147 | -2.49142 |
| user_004 | Walking | 1.446046381789e12 | -1.03405 | -5.51753 | 1.18733 |
| user_004 | Walking | 1.446046381838e12 | -3.52487 | -2.33694 | -0.15388 |
| user_004 | Walking | 1.446046381935e12 | 2.2615 | -8.88971 | -5.25048 |
| user_004 | Walking | 1.446046382072e12 | -1.26397 | -6.13065 | -6.63001 |
| user_004 | Walking | 1.446046382081e12 | -2.60518 | -8.23827 | -4.02423 |
| user_004 | Walking | 1.44604638221e12 | -4.09967 | -3.40991 | 5.096 |
| user_004 | Walking | 1.44604638234e12 | -4.63616 | -4.5212 | 3.10335 |
| user_004 | Walking | 1.446046382372e12 | -4.02303 | -6.74378 | 1.49389 |
| user_004 | Walking | 1.44604638251e12 | -0.305964 | -10.07764 | -0.383802 |
| user_004 | Walking | 1.446046382591e12 | 1.41845 | -11.38053 | -1.18853 |
| user_004 | Walking | 1.44604638268e12 | 2.33814 | -11.72542 | 1.30229 |
| user_004 | Walking | 1.446046382703e12 | 2.98958 | -13.52647 | 1.14901 |
| user_004 | Walking | 1.44604638272e12 | 0.268841 | -16.17057 | -5.99e-4 |
| user_004 | Walking | 1.446046382736e12 | -4.21464 | -15.40417 | -2.18486 |
| user_004 | Walking | 1.446046382858e12 | 2.22318 | -7.89339 | -2.14654 |
| user_004 | Walking | 1.446046382939e12 | -1.45557 | -5.90073 | 0.880768 |
| user_004 | Walking | 1.446046382947e12 | -1.95374 | -5.55585 | 0.880768 |
| user_004 | Walking | 1.446046383084e12 | 1.22685 | -8.42987 | -8.08618 |
| user_004 | Walking | 1.446046383109e12 | 3.25783 | -15.05928 | -6.89825 |
| user_004 | Walking | 1.446046383157e12 | -0.344284 | -11.61046 | -6.82161 |
| user_004 | Walking | 1.44604638323e12 | -2.52854 | -16.66874 | -1.53341 |
| user_004 | Jumping | 1.446046441725e12 | 0.767005 | 1.30349 | 1.03405 |
| user_004 | Jumping | 1.446046441855e12 | 19.50564 | -19.58108 | -6.28513 |
| user_004 | Jumping | 1.446046442243e12 | 0.575403 | 1.15021 | -0.690364 |
| user_004 | Jumping | 1.446046442502e12 | 6.89825 | -11.11229 | -1.57173 |
| user_004 | Jumping | 1.446046442761e12 | -2.14534 | -2.79678 | -2.0699 |
| user_004 | Jumping | 1.446046444187e12 | -1.8771 | 1.57173 | -1.80165 |
| user_004 | Jumping | 1.446046444899e12 | 0.498763 | 1.30349 | 1.18733 |
| user_004 | Jumping | 1.446046445222e12 | -0.267643 | 3.2195 | -2.10822 |
| user_004 | Jumping | 1.446046445352e12 | 1.61005 | -0.689167 | -0.613724 |
| user_004 | Jumping | 1.446046445417e12 | 0.230521 | 0.958607 | -0.805325 |
| user_004 | Jumping | 1.446046446193e12 | -1.26397 | 0.805325 | 1.18733 |
| user_004 | Jumping | 1.446046446517e12 | 0.652044 | 0.268841 | 0.995729 |
| user_004 | Jumping | 1.446046446712e12 | -2.10702 | -2.18366 | 4.06135 |
| user_004 | Jumping | 1.446046447036e12 | 0.498763 | 0.690364 | -1.34181 |
| user_004 | Jumping | 1.446046448849e12 | -0.535886 | -3.83143 | 4.63616 |
| user_004 | Jumping | 1.446046449367e12 | 2.56806 | -8.08499 | 1.41725 |
| user_004 | Jumping | 1.446046451892e12 | -0.382604 | 0.690364 | 7.6042e-2 |
| user_004 | Jumping | 1.446046452215e12 | -7.6042e-2 | 3.44943 | -2.2615 |
| user_004 | Jumping | 1.446046452538e12 | 18.54763 | -15.40417 | -3.98591 |
| user_004 | Jumping | 1.446046452668e12 | -3.7722e-2 | -5.40257 | 3.67815 |
| user_004 | Jumping | 1.446046453445e12 | 3.0279 | -1.45557 | -7.7239e-2 |
| user_004 | Jumping | 1.446046453575e12 | -0.305964 | 0.690364 | -1.07357 |
| user_004 | Jumping | 1.44604645364e12 | 8.31611 | -4.5212 | 0.497565 |
| user_004 | Jumping | 1.446046453834e12 | 1.18853 | 5.05888 | 0.344284 |
| user_004 | Jumping | 1.44604645487e12 | 12.22478 | -14.1396 | -1.03525 |
| user_004 | Jumping | 1.44604645623e12 | 1.41845 | -3.71647 | -0.958607 |
| user_004 | Jumping | 1.446046456488e12 | 18.58595 | -16.28553 | -4.714 |
| user_004 | Jumping | 1.446046456813e12 | 0.881966 | -0.765807 | -0.1922 |
| user_006 | Walking | 1.446046893889e12 | -3.33327 | -5.40257 | -3.60271 |
| user_006 | Walking | 1.446046894277e12 | -5.70913 | -6.36057 | -2.29982 |
| user_006 | Walking | 1.446046895636e12 | -8.96635 | -10.001 | -1.99325 |
| user_006 | Walking | 1.446046895895e12 | -5.44089 | -7.05034 | -1.76333 |
| user_006 | Walking | 1.446046896218e12 | -6.51385 | -11.11229 | -2.98958 |
| user_006 | Walking | 1.446046896477e12 | -5.70913 | -5.21096 | -2.03158 |
| user_006 | Walking | 1.446046896607e12 | -6.16897 | -6.93538 | -2.0699 |
| user_006 | Walking | 1.446046896865e12 | -4.67448 | -9.57948 | -0.307161 |
| user_006 | Walking | 1.446046898354e12 | -8.35323 | -8.50651 | -6.55337 |
| user_006 | Walking | 1.446046898872e12 | -8.54483 | -9.6178 | -1.80165 |
| user_006 | Walking | 1.446046899908e12 | -6.9737 | -8.85139 | -0.958607 |
| user_006 | Walking | 1.446046899973e12 | -9.4262 | -10.15428 | -1.76333 |
| user_006 | Walking | 1.446046900361e12 | -3.37159 | -4.40624 | -2.98958 |
| user_006 | Walking | 1.446046902044e12 | -7.12698 | -8.69811 | -1.22685 |
| user_006 | Walking | 1.446046903274e12 | -5.51753 | -9.15796 | -1.38013 |
| user_006 | Walking | 1.446046903727e12 | -8.04667 | -7.62514 | -7.66466 |
| user_006 | Walking | 1.446046906122e12 | -7.01202 | -6.89706 | -0.843646 |
| user_006 | Walking | 1.446046907157e12 | -6.28393 | -7.43354 | -0.345482 |
| user_006 | Walking | 1.446046908906e12 | -2.91174 | -3.67815 | -2.68302 |
| user_006 | Walking | 1.446046910005e12 | -2.72014 | -4.138 | -2.60638 |
Feature Selection on Running Windows
- ETL Using SparkSQL Windows
A Markov Process Assumption
This is sensible since the subjects are not instantaneously changing between the activities of interest: sitting, walking, jogging, etc. Thus it makes sense to try and use the most recent accelerometer readings (from the immediate past) to predict the current activity.
See the following for a crash introduction to windows:
// Import the window functions.
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
// Create a window specification
val windowSize = 10
val wSpec1 = Window.partitionBy("user_id","activity").orderBy("timeStampAsLong").rowsBetween(-windowSize, 0)
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
windowSize: Int = 10
wSpec1: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@184df46f
// Calculate the moving window statistics from data
val dataFeatDF = dataDF
.withColumn( "meanX", mean($"x").over(wSpec1) )
.withColumn( "meanY", mean($"y").over(wSpec1) )
.withColumn( "meanZ", mean($"z").over(wSpec1) )
//resultant = 1/n * ∑ √(x² + y² + z²)
.withColumn( "SqX", pow($"x",2.0) )
.withColumn( "SqY", pow($"y",2.0) )
.withColumn( "SqZ", pow($"z",2.0) )
.withColumn( "resultant", pow( $"SqX"+$"SqY"+$"SqZ",0.50 ) )
.withColumn( "meanResultant", mean("resultant").over(wSpec1) )
// (1 / n ) * ∑ |b - mean_b|, for b in {x,y,z}
.withColumn( "absDevFromMeanX", abs($"x" - $"meanX") )
.withColumn( "absDevFromMeanY", abs($"y" - $"meanY") )
.withColumn( "absDevFromMeanZ", abs($"z" - $"meanZ") )
.withColumn( "meanAbsDevFromMeanX", mean("absDevFromMeanX").over(wSpec1) )
.withColumn( "meanAbsDevFromMeanY", mean("absDevFromMeanY").over(wSpec1) )
.withColumn( "meanAbsDevFromMeanZ", mean("absDevFromMeanZ").over(wSpec1) )
//standard deviation = √ variance = √ 1/n * ∑ (x - u)² with u = mean x
.withColumn( "sqrDevFromMeanX", pow($"absDevFromMeanX",2.0) )
.withColumn( "sqrDevFromMeanY", pow($"absDevFromMeanY",2.0) )
.withColumn( "sqrDevFromMeanZ", pow($"absDevFromMeanZ",2.0) )
.withColumn( "varianceX", mean("sqrDevFromMeanX").over(wSpec1) )
.withColumn( "varianceY", mean("sqrDevFromMeanY").over(wSpec1) )
.withColumn( "varianceZ", mean("sqrDevFromMeanZ").over(wSpec1) )
.withColumn( "stddevX", pow($"varianceX",0.50) )
.withColumn( "stddevY", pow($"varianceY",0.50) )
.withColumn( "stddevZ", pow($"varianceZ",0.50) )
dataFeatDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 27 more fields]
display(dataFeatDF.sample(false,0.1))
| user_id | activity | timeStampAsLong | x | y | z | meanX | meanY | meanZ | SqX | SqY | SqZ | resultant | meanResultant | absDevFromMeanX | absDevFromMeanY | absDevFromMeanZ | meanAbsDevFromMeanX | meanAbsDevFromMeanY | meanAbsDevFromMeanZ | sqrDevFromMeanX | sqrDevFromMeanY | sqrDevFromMeanZ | varianceX | varianceY | varianceZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_001 | Jogging | 1.446047139921e12 | 2.2615 | -11.34221 | 1.11069 | 4.769736363636363 | -9.105701545454545 | -0.27929245454545465 | 5.114382249999999 | 128.6457276841 | 1.2336322760999998 | 11.618680743105045 | 12.494198224372735 | 2.508236363636363 | 2.2365084545454543 | 1.3899824545454547 | 3.004076849075167 | 6.053738699439197 | 2.006725409359832 | 6.291249655867765 | 5.001970067253296 | 1.932051223944207 | 13.276512488367912 | 55.13839612187212 | 6.2454340102018895 | 3.6436948950711985 | 7.4255232894303225 | 2.499086635193324 |
| user_001 | Jogging | 1.446047140245e12 | -1.34061 | -19.58108 | 3.21831 | 2.0873136363636364 | -7.7122354545454535 | -0.18871709090909092 | 1.7972351721000002 | 383.4186939664 | 10.357519256099998 | 19.889028342143813 | 12.629144725518342 | 3.4279236363636363 | 11.868844545454547 | 3.4070270909090907 | 4.753278789846518 | 7.574200039521841 | 2.467566211839171 | 11.750660456740496 | 140.86947084416613 | 11.607833598188462 | 42.19660591740247 | 77.92220654221632 | 8.576921435389462 | 6.495891464410598 | 8.827355580365861 | 2.9286381537140196 |
| user_001 | Jogging | 1.446047140569e12 | 4.63736 | -15.05928 | 0.420925 | 2.9826163636363634 | -7.151363545454545 | 0.23280709090909094 | 21.505107769600002 | 226.78191411839998 | 0.177177855625 | 15.762747214354006 | 13.153262839097122 | 1.6547436363636367 | 7.907916454545455 | 0.18811790909090906 | 6.333303553719008 | 7.918052239669422 | 2.1468873636363637 | 2.7381765020859516 | 62.53514265207075 | 3.538834772073553e-2 | 59.6090899788082 | 81.98783760943543 | 7.147677048385896 | 7.720692325096772 | 9.054713557558594 | 2.6735139888143276 |
| user_001 | Jogging | 1.446047141021e12 | 1.49509 | -19.54276 | 4.67448 | 3.167250909090909 | -7.496246999999999 | -0.2270356363636364 | 2.2352941081 | 381.91946841760006 | 21.850763270399998 | 20.14957879947122 | 11.485779570157224 | 1.6721609090909089 | 12.046513000000001 | 4.901515636363636 | 5.198262314049587 | 8.112503272727274 | 2.16874 | 2.796122105891735 | 145.11847545916902 | 24.024855533517222 | 35.721060701640276 | 78.69404505664698 | 7.111298197847909 | 5.976709855902349 | 8.870966410524108 | 2.666701745199097 |
| user_001 | Jogging | 1.446047141216e12 | 9.50404 | 0.805325 | -1.91661 | 3.8500481818181824 | -9.464517545454546 | -0.1190421818181818 | 90.3267763216 | 0.6485483556249999 | 3.6733938920999996 | 9.728757298305112 | 13.021301578961113 | 5.653991818181817 | 10.269842545454546 | 1.7975678181818182 | 5.054480909090909 | 7.997542595041323 | 2.5874130247933884 | 31.96762348006693 | 105.46966590842831 | 3.2312500609629424 | 29.863606153648753 | 78.64192339576647 | 9.26849699920601 | 5.464760393068369 | 8.86802815713654 | 3.0444206344074747 |
| user_001 | Jogging | 1.446047142279e12 | 1.87829 | -12.83671 | 4.29128 | 3.6758644545454544 | -10.067191818181819 | 1.783038181818182 | 3.5279733241 | 164.7811236241 | 18.415084038400003 | 13.66470566776321 | 15.459400519749591 | 1.7975744545454544 | 2.7695181818181815 | 2.5082418181818182 | 3.7047198760330575 | 8.578998289256198 | 2.3112528677685953 | 3.231273919634388 | 7.670230959421486 | 6.291277018476033 | 30.205942270589105 | 97.09991623522866 | 7.76694319591596 | 5.49599329244397 | 9.85392897453745 | 2.786923607836419 |
| user_001 | Jogging | 1.446047142286e12 | 1.38013 | -11.03565 | 1.37893 | 3.4668444545454546 | -9.293818181818182 | 1.6924627272727273 | 1.9047588169000003 | 121.78557092250001 | 1.9014479449 | 11.206773741104083 | 14.657520425434432 | 2.0867144545454543 | 1.7418318181818186 | 0.3135327272727273 | 3.8557840495867763 | 7.7102990413223145 | 2.1829909090909094 | 4.354377214808933 | 3.03397808283058 | 9.83027710710744e-2 | 30.585373629779973 | 85.7726380372951 | 7.505552122014648 | 5.530404472530013 | 9.261351847181658 | 2.739626274150299 |
| user_001 | Jogging | 1.446047142375e12 | -4.06135 | 3.94759 | -2.8363 | -1.413767909090909 | -1.8387784545454544 | 0.4244077272727273 | 16.4945638225 | 15.583466808099999 | 8.04459769 | 6.334242521454321 | 5.009300086899544 | 2.647582090909091 | 5.786368454545454 | 3.2607077272727274 | 2.194390801652892 | 5.256532165289254 | 1.5008256694214879 | 7.009690928102553 | 33.482059891758745 | 10.632214882696076 | 6.146341433895125 | 32.61759539584144 | 3.5185498123723344 | 2.4791816056705334 | 5.711181611176573 | 1.875779787814213 |
| user_001 | Jogging | 1.446047142383e12 | -4.09967 | 5.71033 | -4.63736 | -1.9154151818181817 | -0.5567893636363634 | -0.11904409090909084 | 16.8072941089 | 32.6078687089 | 21.505107769600002 | 8.421417374017274 | 4.991653538371555 | 2.184254818181818 | 6.267119363636363 | 4.5183159090909095 | 2.257413231404958 | 5.807901942148759 | 1.9014471157024795 | 4.770969110750487 | 39.27678511806585 | 20.41517865434401 | 6.3779654324036805 | 36.18450086682646 | 5.373345345458154 | 2.525463409436708 | 6.0153554231505275 | 2.3180477444302467 |
| user_001 | Jogging | 1.446047142554e12 | -4.138 | -13.29655 | -19.54396 | 3.766440545454545 | -2.9570327272727273 | -2.857203727272727 | 17.123044 | 176.7982419025 | 381.96637248159993 | 23.997659435538708 | 9.403610514754291 | 7.904440545454545 | 10.339517272727273 | 16.686756272727273 | 5.648922247933883 | 4.328929578512397 | 5.261284214876033 | 62.48018033662574 | 106.90561743302561 | 278.447834905403 | 40.53182288476939 | 26.28204059887057 | 49.8798457427616 | 6.366460781687843 | 5.126601271687761 | 7.0625665124486865 |
| user_001 | Jogging | 1.446047142594e12 | 1.87829 | -19.58108 | 1.57053 | 6.187586363636364 | -8.304458454545456 | 0.2293225454545458 | 3.5279733241 | 383.4186939664 | 2.4665644809 | 19.73355598394268 | 16.627602201403956 | 4.309296363636364 | 11.276621545454544 | 1.3412074545454542 | 5.647655338842975 | 5.920330975206611 | 9.059428404958679 | 18.570035149649595 | 127.16219347940964 | 1.7988374361282966 | 42.971910058386946 | 51.737361373205154 | 117.22960161633895 | 6.555296336428045 | 7.192868786041155 | 10.827261963042131 |
| user_001 | Jogging | 1.446047142691e12 | 13.56599 | -19.58108 | 2.41358 | 5.995984909090908 | -19.581079999999996 | 3.625896636363636 | 184.03608468009998 | 383.4186939664 | 5.8253684164 | 23.943269347833432 | 22.646530680721458 | 7.570005090909091 | 3.552713678800501e-15 | 1.212316636363636 | 6.413111057851239 | 2.7400603553719036 | 5.932997801652892 | 57.304977076389555 | 1.2621774483536189e-29 | 1.4697116268040404 | 53.08331806979702 | 14.682762675546309 | 41.69279164792213 | 7.285829950650579 | 3.831809321397179 | 6.456995558920737 |
| user_001 | Jogging | 1.446047142708e12 | 7.9329 | -19.54276 | 4.36792 | 7.382483636363636 | -19.57759636363636 | 2.2672675454545446 | 62.93090241 | 381.91946841760006 | 19.0787251264 | 21.539013346808623 | 22.445030614743192 | 0.5504163636363639 | 3.483636363635867e-2 | 2.1006524545454552 | 6.184139041322314 | 1.4485709917355396 | 4.827092694214876 | 0.30295817335867803 | 1.2135722314046127e-3 | 4.412740734787846 | 51.846041086663654 | 5.329512547258234 | 30.756938086205707 | 7.2004195076859 | 2.3085737040991856 | 5.545893804086561 |
| user_001 | Jogging | 1.446047142748e12 | 5.90193 | -11.15061 | 0.574206 | 8.772467272727276 | -17.264443636363637 | 1.7830378181818183 | 34.8327777249 | 124.33610337210001 | 0.329712530436 | 12.629275261369356 | 19.990844360519667 | 2.870537272727276 | 6.113833636363637 | 1.2088318181818183 | 6.053026280991736 | 1.8675605785123965 | 3.4662473884297524 | 8.239984234116548 | 37.37896173313141 | 1.4612743646487605 | 48.73046402970053 | 9.197896057389853 | 18.544546604793048 | 6.9807208818073025 | 3.032803333121001 | 4.306337957568245 |
| user_001 | Jogging | 1.44604714278e12 | 8.08618 | -5.05768 | -6.66833 | 6.333900909090909 | -13.038758181818183 | 0.9434755454545454 | 65.38630699240001 | 25.580126982400003 | 44.4666249889 | 11.637571007890779 | 15.478486935501174 | 1.7522790909090915 | 7.981078181818183 | 7.611805545454546 | 2.751777404958678 | 4.34032950413223 | 3.3056813305785124 | 3.0704820124371923 | 63.69760894429423 | 57.93958366181258 | 11.526650756635524 | 26.58225658028617 | 15.31464409781463 | 3.3950921573111272 | 5.155798345580068 | 3.913392913804418 |
| user_001 | Jogging | 1.446047142805e12 | 8.35443 | 1.15021 | -5.71033 | 6.549888181818181 | -8.025764454545454 | -2.087313545454545 | 69.7965006249 | 1.3229830441 | 32.6078687089 | 10.184662605010537 | 12.622237469497941 | 1.8045418181818196 | 9.175974454545454 | 3.623016454545455 | 2.6760871900826455 | 6.690536685950412 | 4.164880024793389 | 3.256371173566947 | 84.19850719047074 | 13.126248229907118 | 10.786670222573182 | 48.26457948474828 | 22.346682708536058 | 3.2843066578157987 | 6.947271369735623 | 4.727227803748837 |
| user_001 | Jogging | 1.446047142829e12 | 7.62634 | 5.44208 | -4.44576 | 7.870197272727273 | -2.8978099090909075 | -4.320343545454545 | 58.1610617956 | 29.6162347264 | 19.7647819776 | 10.370249683570787 | 11.335677718498262 | 0.24385727272727298 | 8.339889909090907 | 0.12541645454545503 | 1.4216515702479346 | 7.973158644628099 | 3.7759768181818187 | 5.9466369461983595e-2 | 69.55376369575633 | 1.5729287070752187e-2 | 2.692849359760634 | 65.2374106261797 | 21.077675670898802 | 1.6409903594356166 | 8.076967910433945 | 4.591042982906913 |
| user_001 | Jogging | 1.446047142869e12 | 0.1922 | 1.41845 | -4.63736 | 6.093528181818181 | 2.8014646363636366 | -5.215646363636364 | 3.694084e-2 | 2.0120004025 | 21.505107769600002 | 4.853251385627989 | 9.218776944004022 | 5.901328181818181 | 1.3830146363636366 | 0.578286363636364 | 2.520273636363636 | 6.500834239669422 | 1.8697786859504135 | 34.82567430952148 | 1.912729484396042 | 0.334415118367769 | 9.10457395356957 | 52.70820165063524 | 8.841695357028382 | 3.0173786559809774 | 7.260041435875917 | 2.9734988409327454 |
| user_001 | Jogging | 1.446047142886e12 | -2.4519 | -1.53221 | -1.57173 | 4.240219090909091 | 2.961713181818182 | -4.400469090909092 | 6.011813610000001 | 2.3476674841 | 2.4703351929000004 | 3.290868621959862 | 7.8287111805749525 | 6.692119090909091 | 4.493923181818182 | 2.828739090909092 | 3.319933140495868 | 5.620416438016528 | 1.1214241239669427 | 44.784457926909916 | 20.195345564082857 | 8.001764844437195 | 16.078224532538766 | 41.51470011538472 | 2.4825790390895945 | 4.009766144370363 | 6.443190212572086 | 1.5756202077561694 |
| user_001 | Jogging | 1.446047142918e12 | 3.8919e-2 | -7.5485 | 2.41358 | 0.4081871818181817 | -0.3059640909090906 | -2.2266609090909095 | 1.514688561e-3 | 56.97985224999999 | 5.8253684164 | 7.925070053631134 | 6.315034135405699 | 0.3692681818181817 | 7.242535909090909 | 4.64024090909091 | 4.338746115702478 | 4.679827628099175 | 2.179507727272728 | 0.1363589901033057 | 52.45432639447129 | 21.53183569440083 | 23.128383278630352 | 26.851247524683767 | 8.024400492232104 | 4.809197779113513 | 5.181818939782031 | 2.832737279069858 |
| user_001 | Jogging | 1.446047143048e12 | 4.86728 | -19.58108 | 0.957409 | 10.678030909090909 | -19.525341818181815 | -1.6205011818181818 | 23.6904145984 | 383.4186939664 | 0.9166319932809999 | 20.19964704043318 | 22.590165936575104 | 5.810750909090909 | 5.573818181818524e-2 | 2.577910181818182 | 4.778637975206611 | 4.502477933884299 | 1.594884165289256 | 33.764826127500825 | 3.1067449123970757e-3 | 6.645620905521852 | 25.40047323918317 | 31.66346295541629 | 3.9266513036706896 | 5.0398882169333055 | 5.627029674296759 | 1.9815779832423173 |
| user_001 | Jogging | 1.44604714308e12 | 3.0279 | -19.58108 | 5.2876 | 9.615512727272726 | -19.556694545454544 | 8.649336363636362e-2 | 9.16817841 | 383.4186939664 | 27.958713760000002 | 20.507208150706425 | 22.28960157670052 | 6.5876127272727265 | 2.4385454545456042e-2 | 5.201106636363637 | 5.043396785123966 | 1.4739062809917371 | 1.867876578512397 | 43.396641444525606 | 5.946503933885027e-4 | 27.051510242825863 | 28.349099778321065 | 5.239003576490311 | 5.987174106897974 | 5.324387267876094 | 2.2888869732886135 | 2.4468702676884964 |
| user_001 | Jogging | 1.446047143153e12 | 4.25415 | -0.612526 | 0.727487 | 1.9828029090909092 | -11.161062363636363 | 3.841882454545454 | 18.0977922225 | 0.375188100676 | 0.529237335169 | 4.359153318976634 | 12.602918806903675 | 2.271347090909091 | 10.548536363636362 | 3.114395454545454 | 4.325445371900826 | 5.669189173553718 | 2.3229701652892563 | 5.15901760738119 | 111.27161941495865 | 9.699459047293386 | 25.218244932986796 | 46.66018201959887 | 7.700625972606309 | 5.02177706922428 | 6.8308258665844255 | 2.7750001752443745 |
| user_001 | Jogging | 1.446047143242e12 | -5.74745 | 4.86728 | -1.80165 | -3.260111181818182 | 4.99269 | -3.421557272727273 | 33.0331815025 | 23.6904145984 | 3.2459427224999997 | 7.744000182295968 | 7.55742066211505 | 2.4873388181818177 | 0.12540999999999958 | 1.6199072727272732 | 3.4627619504132228 | 5.632767752066116 | 3.413991008264463 | 6.186854396434121 | 1.5727668099999894e-2 | 2.6240995722347122 | 14.443867894436066 | 49.65072869425762 | 18.454665286095363 | 3.8005088994022977 | 7.0463273195514855 | 4.295889347515292 |
| user_001 | Jogging | 1.446047143484e12 | 6.24681 | -19.58108 | 3.63983 | -0.31641409090909084 | -19.340707272727265 | 3.7861454545454545 | 39.0226351761 | 383.4186939664 | 13.248362428899998 | 20.873181156004947 | 21.793673526369936 | 6.563224090909091 | 0.24037272727273518 | 0.14631545454545458 | 4.006847198347107 | 4.57468519834711 | 5.562779223140495 | 43.075910467489464 | 5.7779048016532726e-2 | 2.1408212238842986e-2 | 22.94847834491234 | 30.761658745192324 | 62.221786747632834 | 4.7904570079390485 | 5.546319387232611 | 7.888078774177704 |
| user_001 | Jogging | 1.446047143533e12 | 11.22845 | -19.58108 | 9.15796 | 4.856828 | -19.581079999999996 | 3.622413636363636 | 126.07808940250001 | 383.4186939664 | 83.86823136159998 | 24.359084850020537 | 22.40485202749899 | 6.371622 | 3.552713678800501e-15 | 5.535546363636364 | 6.480882214876033 | 0.7306194214876078 | 5.342358561983472 | 40.597566910884005 | 1.2621774483536189e-29 | 30.64227354396777 | 48.619000492080474 | 1.6596915876648444 | 41.51160187686411 | 6.9727326416606905 | 1.2882901799147755 | 6.442949780718775 |
| user_001 | Jogging | 1.44604714363e12 | 5.82529 | 3.14286 | 3.21831 | 11.935633636363635 | -4.315663545454545 | -2.742242818181819 | 33.9340035841 | 9.877568979600001 | 10.357519256099998 | 7.359965476807618 | 14.48403310791005 | 6.110343636363635 | 7.458523545454545 | 5.960552818181819 | 4.2234690247933875 | 8.366494983471073 | 7.1579815123966934 | 37.33629935444957 | 55.629573478099836 | 35.52818989833523 | 22.091004368014264 | 72.82379547301899 | 68.08732499721447 | 4.700106846446607 | 8.53368592537943 | 8.251504408119436 |
| user_001 | Jogging | 1.446047143663e12 | 3.87095 | 6.05521 | 0.650847 | 7.797039090909089 | 1.4045146363636363 | 8.300872727272714e-2 | 14.9842539025 | 36.6655681441 | 0.42360181740899994 | 7.216191784037409 | 9.495099067442254 | 3.926089090909089 | 4.6506953636363635 | 0.5678382727272728 | 4.072720743801653 | 7.843313049586777 | 4.4008187355371895 | 15.414175549755356 | 21.628967365348768 | 0.3224403039738927 | 20.156069219244248 | 65.0230244160993 | 25.9053624014897 | 4.489551115562028 | 8.063685535541381 | 5.08973107359217 |
| user_001 | Jogging | 1.446047143687e12 | 0.805325 | 5.40376 | -1.18853 | 4.815022272727273 | 3.9789428181818183 | 1.4346706363636363 | 0.6485483556249999 | 29.2006221376 | 1.4126035609000003 | 5.591222947989554 | 7.279610887559767 | 4.009697272727273 | 1.4248171818181818 | 2.623200636363636 | 4.808407685950414 | 5.901961702479338 | 3.262293380165289 | 16.07767221891653 | 2.0301040016043057 | 6.881181578618586 | 23.86087788087626 | 40.88601350062889 | 14.184341683283828 | 4.8847597567205145 | 6.394217192168944 | 3.7662105203086864 |
| user_001 | Jogging | 1.446047143816e12 | 5.25048 | -19.58108 | 3.21831 | 3.5051649090909094 | -14.543700909090909 | 1.8248400000000002 | 27.567540230399995 | 383.4186939664 | 10.357519256099998 | 20.526659578531035 | 15.631401506522486 | 1.7453150909090902 | 5.037379090909091 | 1.3934699999999995 | 2.619398677685951 | 9.328620743801652 | 3.4855632314049587 | 3.046124766555006 | 25.375188105528103 | 1.9417586408999987 | 8.646362838626812 | 93.54406741070962 | 19.849020919530062 | 2.9404698329734336 | 9.671818206041179 | 4.455224003294342 |
| user_001 | Jogging | 1.446047143849e12 | 7.24314 | -19.58108 | 3.14167 | 5.365441818181818 | -19.055046363636357 | 3.281012727272727 | 52.4630770596 | 383.4186939664 | 9.8700903889 | 21.112836413303164 | 20.327333710024067 | 1.8776981818181824 | 0.5260336363636426 | 0.139342727272727 | 2.544658710743802 | 6.967329090909092 | 2.8046661157024797 | 3.525750462003308 | 0.276711386585957 | 1.9416395643801578e-2 | 8.143183079260638 | 69.0138884550936 | 12.59461059977295 | 2.8536263033657083 | 8.30745980761229 | 3.5488886429096294 |
| user_001 | Jogging | 1.446047144019e12 | -5.40257 | 6.66833 | -3.06622 | -2.521573636363637 | 1.8539080909090908 | -1.5299249090909088 | 29.187762604899998 | 44.4666249889 | 9.4017050884 | 9.113511545074159 | 6.109509864671735 | 2.880996363636363 | 4.8144219090909095 | 1.536295090909091 | 3.2211229834710746 | 9.29378385950413 | 2.721057818181819 | 8.300140047285947 | 23.17865831873456 | 2.3602026063513724 | 10.905233028506162 | 91.25062469758161 | 9.55560119495235 | 3.3023072280613386 | 9.552519285381297 | 3.0912135472905056 |
| user_001 | Jogging | 1.446047144099e12 | -1.22565 | 2.8363 | -1.87829 | -3.782661818181819 | 6.0168872727272715 | -1.9479664545454547 | 1.5022179224999999 | 8.04459769 | 3.5279733241 | 3.6159077610746655 | 7.549696857727938 | 2.557011818181819 | 3.1805872727272715 | 6.967645454545468e-2 | 1.4640892479338838 | 1.9907564793388437 | 1.209148776859504 | 6.538309438321491 | 10.116135399434702 | 4.854808318024812e-3 | 2.7824785542542148 | 6.021358653040184 | 2.015921675846954 | 1.6680763034868082 | 2.453845686476675 | 1.4198315660130092 |
| user_001 | Jogging | 1.446047144164e12 | 3.0279 | -3.63983 | 5.47921 | 2.8641704545454547 | 0.45695827272727274 | -0.5614681818181815 | 9.16817841 | 13.248362428899998 | 30.021742224100002 | 7.241428247452294 | 6.395763415462877 | 0.1637295454545451 | 4.096788272727273 | 6.040678181818182 | 3.994813181818181 | 3.586591016528926 | 3.1257973966942147 | 2.680736405475195e-2 | 16.78367415155571 | 36.48979289629421 | 22.083146815942428 | 14.230722467376609 | 13.901029976940652 | 4.699270881311528 | 3.7723629819221545 | 3.728408504568759 |
| user_001 | Jogging | 1.446047144205e12 | 11.84158 | -9.11964 | 19.58108 | 4.177513272727273 | -4.695380454545455 | 2.410097272727273 | 140.2230168964 | 83.1678337296 | 383.4186939664 | 24.633504513008294 | 11.547958818299337 | 7.664066727272727 | 4.424259545454546 | 17.170982727272726 | 5.005709380165289 | 4.785287421487603 | 7.658678082644627 | 58.73791880008889 | 19.574072525545663 | 294.8426478202983 | 30.767078886801734 | 24.89549485314188 | 85.72207552243663 | 5.54680799079991 | 4.98953854110196 | 9.258621685890219 |
| user_001 | Jogging | 1.446047144294e12 | 11.95654 | -19.58108 | 3.75479 | 3.0906112727272728 | -19.267550909090904 | 6.416312 | 142.9588487716 | 383.4186939664 | 14.098447944099998 | 23.248139510122094 | 21.319530424095966 | 8.865928727272728 | 0.3135290909090962 | 2.6615219999999997 | 3.267994859504132 | 5.688191446280993 | 3.850399157024793 | 78.60469219707981 | 9.830049084628431e-2 | 7.083699356483999 | 16.635098001876802 | 45.33735918091032 | 19.356079423879518 | 4.078614716037396 | 6.733302249335783 | 4.399554457428561 |
| user_001 | Jogging | 1.446047144318e12 | 7.51138 | -19.58108 | 9.46452 | 4.619939454545455 | -19.581079999999996 | 6.872671818181818 | 56.4208295044 | 383.4186939664 | 89.5771388304 | 23.009056093225553 | 22.07631496113507 | 2.891440545454545 | 3.552713678800501e-15 | 2.5918481818181824 | 4.7118147438016535 | 2.8803568595041344 | 2.8274684132231402 | 8.360428427898476 | 1.2621774483536189e-29 | 6.717676997594218 | 29.996919431021933 | 16.063989735606317 | 8.899686623597681 | 5.476944351645535 | 4.007990735469122 | 2.983234255568557 |
| user_001 | Jogging | 1.446047144334e12 | 2.10822 | -19.58108 | 6.43721 | 4.919534545454545 | -19.581079999999996 | 6.782096363636364 | 4.444591568400001 | 383.4186939664 | 41.43767258410001 | 20.71957910091081 | 22.086492683781803 | 2.811314545454545 | 3.552713678800501e-15 | 0.3448863636363635 | 4.764385933884298 | 1.5176105785123994 | 2.3502069586776853 | 7.903489473484295 | 1.2621774483536189e-29 | 0.11894660382231395 | 30.15813492282921 | 5.744719094941251 | 6.931913594880021 | 5.49164227921204 | 2.3968143638882946 | 2.6328527484232804 |
| user_001 | Jogging | 1.446047144358e12 | 0.268841 | -14.48448 | 1.26397 | 5.449051181818183 | -18.745 | 5.249283636363637 | 7.2275483281e-2 | 209.8001608704 | 1.5976201609 | 14.542010057573918 | 20.811476939808895 | 5.1802101818181825 | 4.260520000000001 | 3.9853136363636366 | 4.9189340495867775 | 1.0736021487603329 | 2.2425308925619833 | 26.83457752781277 | 18.152030670400013 | 15.882724780185953 | 31.661174239479916 | 3.0665480847950435 | 6.424955978969012 | 5.626826302586558 | 1.751156213704261 | 2.5347496876356472 |
| user_001 | Jogging | 1.446047144432e12 | 11.38173 | 3.67935 | -9.88724 | 5.041460454545454 | -7.349934545454545 | -3.8117286363636365 | 129.5437777929 | 13.5376164225 | 97.75751481760001 | 15.518985438262387 | 12.73755665238099 | 6.340269545454545 | 11.029284545454544 | 6.075511363636364 | 3.7813608181818186 | 7.248872066115704 | 5.965617710743802 | 40.19901790901838 | 121.64511758460246 | 36.911838329674595 | 18.001369432075183 | 58.53190460909211 | 39.68507574973153 | 4.242802073167588 | 7.650614655639905 | 6.299609174364036 |
| user_001 | Jogging | 1.44604714457e12 | 3.44943 | -17.32018 | 1.49389 | 2.223177454545455 | -5.730029545454546 | -0.4325736363636365 | 11.8985673249 | 299.98863523240004 | 2.2317073320999996 | 17.72340006571538 | 8.926374908928052 | 1.2262525454545452 | 11.590150454545455 | 1.9264636363636365 | 1.3424779999999998 | 5.841155958677685 | 3.5812076942148754 | 1.5036953052337514 | 134.3315875590002 | 3.7112621422314054 | 3.7642855529851604 | 51.2352123894596 | 17.394233076215162 | 1.940176680868307 | 7.157877645605546 | 4.1706394085577765 |
| user_001 | Jogging | 1.446047144626e12 | 3.18118 | -19.58108 | 3.98471 | 3.6688972727272726 | -17.389854545454543 | 3.542288636363637 | 10.1199061924 | 383.4186939664 | 15.877913784100002 | 20.234043440274117 | 18.341820410456116 | 0.4877172727272727 | 2.1912254545454566 | 0.4424213636363632 | 1.1087558842975207 | 8.010527595041323 | 3.06119141322314 | 0.23786813811652893 | 4.801468992647943 | 0.1957366630018591 | 1.601310337728242 | 74.55691742125481 | 15.713996208770943 | 1.2654289145298687 | 8.634634758995588 | 3.964088320001327 |
| user_001 | Jogging | 1.446047144715e12 | 5.67201 | -9.27292 | 0.804128 | 3.6375445454545456 | -17.180836363636367 | 3.099863181818182 | 32.171697440100004 | 85.98704532639998 | 0.646621840384 | 10.89978736521424 | 17.99224847571835 | 2.0344654545454546 | 7.907916363636367 | 2.295735181818182 | 1.2424019008264464 | 2.141185867768598 | 1.2177001818181816 | 4.1390496857388435 | 62.53514121426783 | 5.270400025037761 | 2.150091168155898 | 11.058832569033141 | 2.142394874962366 | 1.466318917615093 | 3.3254823062276455 | 1.4636922063611482 |
| user_001 | Jogging | 1.446047144755e12 | 1.34181 | -2.87342 | 0.420925 | 4.045133636363636 | -10.513101818181816 | 1.086304272727273 | 1.8004540760999999 | 8.2565424964 | 0.177177855625 | 3.1990896248972143 | 11.680282540342732 | 2.7033236363636366 | 7.639681818181817 | 0.6653792727272729 | 1.4530048760330578 | 5.778132809917356 | 1.9492681322314047 | 7.307958682922315 | 58.36473828305783 | 0.44272957657507467 | 2.9269021093244927 | 43.49758092784231 | 5.312242645339325 | 1.7108191340186993 | 6.595269587199776 | 2.3048302855827205 |
| user_001 | Jogging | 1.446047144763e12 | 1.34181 | -1.37893 | -0.15388 | 3.578322727272728 | -8.858360909090909 | 1.0096636363636364 | 1.8004540760999999 | 1.9014479449 | 2.3679054399999996e-2 | 1.9301764363394347 | 9.979759335687318 | 2.2365127272727277 | 7.479430909090908 | 1.1635436363636364 | 1.4216519008264463 | 6.458081074380166 | 1.7788850743801652 | 5.001989179252894 | 55.94188672386445 | 1.3538337937223142 | 2.7758463535522164 | 48.58320699364817 | 4.596412149495224 | 1.6660871386431793 | 6.970165492558134 | 2.1439244738318615 |
| user_001 | Jogging | 1.446047144917e12 | 6.59169 | 2.10822 | 2.2603 | -1.190814909090909 | 5.494338181818182 | -3.383237818181818 | 43.450377056099995 | 4.444591568400001 | 5.1089560899999995 | 7.280379434789096 | 8.801927778002462 | 7.782504909090909 | 3.3861181818181816 | 5.643537818181818 | 2.3958118512396696 | 2.5212229090909086 | 4.374532033057851 | 60.567382660024094 | 11.465796341239669 | 31.84951910524839 | 11.386474285730058 | 7.176292284465379 | 34.127908503264536 | 3.3743850233383355 | 2.678860258480345 | 5.8419096623676525 |
| user_001 | Jogging | 1.446047144965e12 | -1.49389 | -10.80573 | -8.50771 | 1.8608763636363634 | -1.807423636363636 | -0.2583900000000001 | 2.2317073320999996 | 116.76380083290002 | 72.3811294441 | 13.833894520672768 | 10.624124936163746 | 3.3547663636363634 | 8.998306363636365 | 8.249319999999999 | 3.971061611570248 | 5.76831652892562 | 7.454093057851239 | 11.254457354585949 | 80.9695174138587 | 68.05128046239999 | 21.70051268411826 | 45.90407271894876 | 64.16546233071315 | 4.658380908010664 | 6.775254439425043 | 8.010334720266885 |
| user_001 | Jogging | 1.446047145046e12 | -4.17632 | -19.58108 | 5.82409 | 2.0176396363636364 | -16.04516 | 3.5074527272727267 | 17.441648742399998 | 383.4186939664 | 33.9200243281 | 20.85138765254965 | 19.917295300480387 | 6.193959636363636 | 3.535920000000001 | 2.3166372727272733 | 3.7306884876033055 | 6.614528264462811 | 5.751531818181817 | 38.365135976901946 | 12.502730246400006 | 5.366808253389259 | 18.671942642803675 | 48.08396713180992 | 68.08284963383029 | 4.321104331395352 | 6.934260388232469 | 8.251233218969773 |
| user_001 | Jogging | 1.446047145095e12 | 8.39275 | -19.58108 | 7.77842 | 3.1184786363636365 | -19.581079999999996 | 5.907696363636364 | 70.4382525625 | 383.4186939664 | 60.50381769639999 | 22.679523015824206 | 21.447303073035563 | 5.2742713636363625 | 3.552713678800501e-15 | 1.8707236363636355 | 5.020279380165289 | 3.0450398347107455 | 1.37161479338843 | 27.817938417274576 | 1.2621774483536189e-29 | 3.4996069236495835 | 31.002898462194388 | 16.330388050525777 | 2.60981791889955 | 5.568024646335035 | 4.041087483651619 | 1.6154930884716128 |
| user_001 | Jogging | 1.446047145232e12 | 4.36911 | 2.49142 | -3.2195 | 5.825286363636365 | -4.29824409090909 | -3.8256609090909084 | 19.0891221921 | 6.207173616400001 | 10.36518025 | 5.971723039332953 | 9.514844653794894 | 1.4561763636363647 | 6.78966409090909 | 0.6061609090909084 | 2.2583658925619834 | 7.407538256198347 | 3.772808876033058 | 2.120449602013226 | 46.09953846738036 | 0.36743104770991647 | 6.742655839755869 | 55.6615513466919 | 18.947554227282133 | 2.596662442397138 | 7.460666950527406 | 4.3528788436254615 |
| user_001 | Jogging | 1.446047145249e12 | 2.68302 | 3.33447 | -2.22318 | 5.675488181818182 | -1.6436913636363633 | -4.062549999999999 | 7.1985963204 | 11.1186901809 | 4.9425293124000005 | 4.82284312555364 | 8.220254896054264 | 2.9924681818181824 | 4.978161363636364 | 1.8393699999999988 | 2.574429280991736 | 7.315063049586775 | 3.0010186446280986 | 8.954865819194218 | 24.782090562401862 | 3.3832819968999956 | 8.001432070676456 | 54.618597304993834 | 13.682063792776631 | 2.828680270139497 | 7.390439588075518 | 3.698927384090776 |
| user_001 | Jogging | 1.446047145289e12 | 2.18486 | 2.91294 | -0.383802 | 3.344915454545455 | 2.4565831818181816 | -1.9967373636363634 | 4.7736132196000005 | 8.485219443599998 | 0.147303975204 | 3.6614391485321724 | 5.247410582023297 | 1.160055454545455 | 0.4563568181818183 | 1.6129353636363635 | 2.197559173553719 | 4.748552677685951 | 1.925832396694215 | 1.345728657620662 | 0.20826154550103315 | 2.601560487268768 | 5.862908678780401 | 29.34863687704508 | 4.891628359301945 | 2.4213443949137843 | 5.417438220879411 | 2.211702592868658 |
| user_001 | Jogging | 1.446047145346e12 | 4.56072 | -3.83143 | -0.575403 | 3.031387272727273 | 1.1571758181818181 | -2.355556272727273 | 20.8001669184 | 14.6798558449 | 0.331088612409 | 5.984238579444255 | 5.171036039387004 | 1.529332727272727 | 4.988605818181818 | 1.780153272727273 | 1.7041447107438017 | 2.1861589338842973 | 2.350839826446281 | 2.3388585907074373 | 24.886188009197486 | 3.168945674401621 | 3.740026841003832 | 6.758621840631681 | 6.766600337218634 | 1.9339149001452551 | 2.599734955842938 | 2.6012689859410223 |
| user_001 | Jogging | 1.446047145411e12 | 4.9056 | -19.58108 | 3.17999 | 3.7490218181818182 | -10.565356818181819 | 0.7727747272727273 | 24.064911359999996 | 383.4186939664 | 10.1123364001 | 20.435164343026457 | 12.227556908045916 | 1.1565781818181815 | 9.015723181818181 | 2.4072152727272726 | 1.0450998347107436 | 7.69731570247934 | 2.7729970082644626 | 1.3376730906578504 | 81.28326449117375 | 5.794685369251438 | 1.3881511997852738 | 67.60655777999472 | 9.645998022142622 | 1.178198285427913 | 8.222320705250722 | 3.1058007054771917 |
| user_001 | Jogging | 1.446047145476e12 | 7.77962 | -19.58108 | -1.03525 | 6.636979999999999 | -19.46611909090909 | 1.3510629090909094 | 60.522487344400005 | 383.4186939664 | 1.0717425625 | 21.095329432680117 | 20.728654720814475 | 1.142640000000001 | 0.11496090909091095 | 2.3863129090909094 | 2.049979173553719 | 4.984490190082647 | 1.4200686280991737 | 1.3056261696000022 | 1.321601061900869e-2 | 5.694489300093919 | 5.392577351311795 | 40.45396663996184 | 2.8114859492491036 | 2.322192358809191 | 6.360343280040932 | 1.6767486243468648 |
| user_001 | Jogging | 1.446047145516e12 | 3.14286 | -16.13225 | 0.497565 | 6.7345227272727275 | -19.17697454545454 | 0.27809427272727266 | 9.877568979600001 | 260.24949006249994 | 0.24757092922499999 | 16.443072400598524 | 20.407458939320875 | 3.5916627272727273 | 3.0447245454545424 | 0.21947072727272732 | 2.3327892561983465 | 1.3263262809917373 | 1.124907140495868 | 12.900041146480165 | 9.27034755769337 | 4.8167400129619856e-2 | 6.693568337809316 | 3.786078162143052 | 1.979100510279392 | 2.5871931388687077 | 1.9457847162887913 | 1.4068050718843006 |
| user_001 | Jogging | 1.446047145713e12 | 0.537083 | 1.95493 | -9.00587 | -1.1315915454545455 | 6.368736363636364 | -1.8399748181818183 | 0.28845814888899995 | 3.8217513049000003 | 81.1056944569 | 9.231246064897684 | 8.221043321888848 | 1.6686745454545455 | 4.413806363636364 | 7.165895181818182 | 1.1534087933884298 | 4.5578994545454545 | 3.6043262396694216 | 2.7844747386479343 | 19.48168661567686 | 51.35005375680503 | 1.7265291963441747 | 27.517037854864906 | 22.455774584975515 | 1.3139745797937548 | 5.245668485032666 | 4.7387524291711545 |
| user_001 | Jogging | 1.446047145793e12 | 11.07517 | -5.55585 | 15.28921 | 5.438599363636363 | -4.371401454545455 | 0.483630909090909 | 122.6593905289 | 30.867469222500006 | 233.75994242410002 | 19.679603709818448 | 13.06356743224656 | 5.636570636363637 | 1.1844485454545453 | 14.805579090909092 | 4.959471123966942 | 6.52902056198347 | 8.533394933884297 | 31.770928538716777 | 1.402918356829388 | 219.2051722171645 | 31.199410662336536 | 50.721947861364015 | 88.07356490656394 | 5.585643263075125 | 7.121934278085134 | 9.384751723224431 |
| user_001 | Jogging | 1.446047145842e12 | -2.4519 | -19.58108 | 7.1653 | 3.7977930909090905 | -14.17095 | 3.6642172727272726 | 6.011813610000001 | 383.4186939664 | 51.34152409 | 20.99457148089477 | 18.45474752731835 | 6.249693090909091 | 5.4101300000000005 | 3.5010827272727276 | 3.4136741074380157 | 7.961440264462809 | 6.744692446280993 | 39.05866373055682 | 29.269506616900006 | 12.25758026320744 | 15.41577004773268 | 71.44910455936972 | 68.57680084938416 | 3.9262921500739956 | 8.452757216398075 | 8.281111087854343 |
| user_001 | Jogging | 1.446047145882e12 | 3.41111 | -19.58108 | 3.33327 | 1.4741900000000001 | -18.839059999999996 | 6.4372145454545455 | 11.635671432099999 | 383.4186939664 | 11.1106888929 | 20.153537016896067 | 20.81950972711309 | 1.9369199999999998 | 0.7420200000000037 | 3.1039445454545453 | 3.82664652892562 | 5.608066132231406 | 3.3576199173553722 | 3.7516590863999992 | 0.5505936804000054 | 9.634471741257023 | 19.101262520859095 | 41.887305489479154 | 18.40387396009669 | 4.370499115760017 | 6.472040287998766 | 4.289973654942031 |
| user_001 | Jogging | 1.446047146037e12 | 7.1665 | 1.99325 | -1.99325 | 5.05887909090909 | -6.590495181818182 | 5.1656545454545434e-2 | 51.35872225 | 3.9730455625 | 3.9730455625 | 7.70096184739283 | 9.618719497689767 | 2.10762090909091 | 8.583745181818182 | 2.044906545454545 | 1.275970876033058 | 6.988546834710745 | 0.8360788925619833 | 4.442065896437193 | 73.68068134638686 | 4.181642779642842 | 2.080890254091703 | 51.76374093133398 | 1.196293165590975 | 1.4425291172422492 | 7.194702282327878 | 1.0937518756971232 |
| user_001 | Jogging | 1.446047146069e12 | 10.69197 | 7.24314 | -6.66833 | 6.960960909090908 | 6.678845454545429e-2 | -1.9758360000000001 | 114.31822248089999 | 52.4630770596 | 44.4666249889 | 14.534370455214082 | 9.05266860963471 | 3.731009090909091 | 7.176351545454546 | 4.692494 | 2.112051570247934 | 8.11630508264463 | 2.00785705785124 | 13.920428836446282 | 51.50002150394785 | 22.019499940036 | 5.017466863377312 | 66.23105049371274 | 6.894872401938625 | 2.2399702818067277 | 8.138246155881053 | 2.625808904307133 |
| user_001 | Jogging | 1.446047146101e12 | 5.13552 | 2.8363 | -2.2615 | 7.699498181818182 | 3.8883675454545457 | -2.9965497272727273 | 26.373565670399998 | 8.04459769 | 5.114382249999999 | 6.287491201616111 | 9.403020285750388 | 2.5639781818181824 | 1.0520675454545456 | 0.7350497272727274 | 2.158606198347108 | 6.178120487603305 | 2.2564640578512396 | 6.573984116839672 | 1.1068461201987525 | 0.5402981015637109 | 5.51196412323629 | 47.76565157080517 | 7.256989296208922 | 2.347757253899195 | 6.911269895670778 | 2.693879970638804 |
| user_001 | Jogging | 1.446047146133e12 | 3.64103 | -0.727487 | -4.44576 | 6.7345227272727275 | 3.700250636363637 | -3.9162381818181817 | 13.257099460900001 | 0.529237335169 | 19.7647819776 | 5.792332757505305 | 8.94416098242155 | 3.0934927272727273 | 4.427737636363637 | 0.5295218181818182 | 2.5373752066115705 | 4.200032082644628 | 1.8229071818181821 | 9.569697253689256 | 19.604860576471047 | 0.2803933559305785 | 7.398827288804058 | 24.31675077202084 | 6.172232880943373 | 2.7200785446019857 | 4.93120175738337 | 2.4843978910277986 |
| user_001 | Jogging | 1.446047146142e12 | 2.49142 | -2.22198 | -3.41111 | 6.225908181818181 | 3.0348706363636366 | -3.9092709090909086 | 6.207173616400001 | 4.937195120399999 | 11.635671432099999 | 4.772844033582074 | 8.453049060126027 | 3.734488181818181 | 5.2568506363636365 | 0.4981609090909087 | 2.676405371900827 | 3.922289024793389 | 1.6018522727272726 | 13.946401980139665 | 27.63447861303677 | 0.24816429134628062 | 8.224617051718033 | 20.548089963168977 | 5.414473050834811 | 2.867859315189299 | 4.533000106239683 | 2.3269020286283673 |
| user_001 | Jogging | 1.446047146149e12 | 1.72501 | -3.37159 | -1.61005 | 5.49433818181818 | 2.1395688181818184 | -3.44246 | 2.9756595001 | 11.3676191281 | 2.5922610025 | 4.11528123348818 | 7.597571407497298 | 3.76932818181818 | 5.511158818181818 | 1.83241 | 2.7109253719008266 | 3.6952176198347115 | 1.2705879421487603 | 14.207834942248745 | 30.372871519223217 | 3.3577264081000004 | 8.471743534990757 | 17.478061159130093 | 2.993352282994137 | 2.910625969613883 | 4.1806771173017045 | 1.7301307126902685 |
| user_001 | Jogging | 1.446047146182e12 | 2.49142 | -15.59577 | 2.60518 | 3.2822109090909093 | -3.6851184545454547 | -1.6832099999999997 | 6.207173616400001 | 243.2280418929 | 6.7869628323999995 | 16.006941567385695 | 7.485217674617422 | 0.7907909090909091 | 11.910651545454545 | 4.28839 | 2.5221734710743795 | 5.532376049586777 | 1.8400089752066116 | 0.6253502619008264 | 141.86362023723873 | 18.390288792099998 | 7.328542144563707 | 40.53981984784796 | 5.562439379226529 | 2.7071280251520626 | 6.367088804771609 | 2.3584824314008634 |
| user_001 | Jogging | 1.446047146271e12 | 5.74865 | -19.58108 | -0.307161 | 5.564010909090908 | -19.452184545454543 | 0.41047318181818165 | 33.04697682249999 | 383.4186939664 | 9.434787992100001e-2 | 20.409802024243668 | 20.332815201913306 | 0.18463909090909159 | 0.12889545454545726 | 0.7176341818181817 | 1.7611500826446287 | 5.651453752066117 | 1.715229809917355 | 3.4091593891735786e-2 | 1.6614038202480037e-2 | 0.5149988189138511 | 4.313352366296018 | 51.02793158470397 | 3.854774185943344 | 2.076861181277174 | 7.143383762944839 | 1.9633578853442244 |
| user_001 | Jogging | 1.446047146336e12 | 4.02423 | -12.0703 | 7.6042e-2 | 4.881213636363636 | -18.114457272727275 | -0.2374875454545454 | 16.1944270929 | 145.69214208999998 | 5.782385764e-3 | 12.723692528848062 | 18.79045997030551 | 0.8569836363636361 | 6.044157272727276 | 0.31352954545454537 | 1.1033715702479336 | 1.3883985123966964 | 0.7727395950413221 | 0.7344209529950408 | 36.53183713746202 | 9.830077587293383e-2 | 1.4202792273080385 | 5.608324464644631 | 1.0397245837182336 | 1.1917546841980686 | 2.36819012426043 | 1.0196688598355026 |
| user_001 | Jogging | 1.446047146384e12 | -1.22565 | -3.79311 | -0.345482 | 2.494902636363636 | -11.060036363636364 | -0.7112660909090909 | 1.5022179224999999 | 14.387683472099999 | 0.119357812324 | 4.001157233466838 | 11.537486823091678 | 3.720552636363636 | 7.2669263636363635 | 0.36578409090909086 | 1.9315339504132227 | 5.419948677685952 | 0.7800240247933885 | 13.842511919952402 | 52.80821877451322 | 0.13379800116219004 | 5.13675033189447 | 37.824145244424585 | 0.7892765054106935 | 2.26644001286036 | 6.150133758254741 | 0.8884123510007577 |
| user_001 | Jogging | 1.446047146424e12 | -2.37526 | 0.575403 | 2.87342 | -0.35821918181818174 | -4.0334840000000005 | 0.22235490909090913 | 5.6418600676 | 0.331088612409 | 8.2565424964 | 3.77219977949326 | 5.344345142984176 | 2.017040818181818 | 4.608887 | 2.651065090909091 | 2.71440861983471 | 6.813098371900828 | 1.4035992479338844 | 4.068453662211578 | 21.241839378769 | 7.028146116236827 | 8.305992954492837 | 47.69968574953315 | 2.723286739214193 | 2.882011962933679 | 6.9064959096153204 | 1.6502383886015357 |
| user_001 | Jogging | 1.446047146458e12 | -3.7722e-2 | 2.49142 | 2.8351 | -1.5356972727272726 | -0.4696949090909092 | 1.8213564545454546 | 1.422949284e-3 | 6.207173616400001 | 8.03779201 | 3.774438842488245 | 3.68460736360343 | 1.4979752727272726 | 2.9611149090909095 | 1.0137435454545456 | 2.3204369421487603 | 5.309738752066116 | 1.6677247190082645 | 2.2439299177023466 | 8.768201504840466 | 1.0276759759507523 | 6.8630149024078095 | 30.7303997650691 | 3.57256734741414 | 2.6197356550628936 | 5.543500677827063 | 1.890123632838376 |
| user_001 | Jogging | 1.446047146522e12 | 1.95493 | 3.2195 | -2.03158 | 0.6415932727272727 | 3.174216363636364 | 0.9469572727272724 | 3.8217513049000003 | 10.36518025 | 4.127317296399999 | 4.279515025245267 | 3.9981079876247936 | 1.3133367272727274 | 4.528363636363597e-2 | 2.978537272727272 | 1.4387527107438016 | 2.1126830826446277 | 1.7142794049586776 | 1.7248533592034385 | 2.050607722314014e-3 | 8.871684285025616 | 2.352517119129482 | 5.5079197320681335 | 3.7086289790114004 | 1.5337917456843617 | 2.3468957650624653 | 1.9257800962237097 |
| user_001 | Jogging | 1.446047146546e12 | 5.13552 | 1.72501 | -3.56439 | 1.9862869090909092 | 3.1951181818181813 | -0.714750909090909 | 26.373565670399998 | 2.9756595001 | 12.7048760721 | 6.4849133565993 | 4.507092480087424 | 3.1492330909090906 | 1.4701081818181814 | 2.8496390909090907 | 1.9106310247933884 | 1.4853064545454548 | 2.1149014958677683 | 9.917669060876825 | 2.1612180662487592 | 8.120442948437189 | 3.988080240379295 | 2.7498252119819964 | 5.482809105983013 | 1.9970178367704419 | 1.6582596937699463 | 2.3415399005746225 |
| user_001 | Jogging | 1.446047146571e12 | 4.63736 | -1.49389 | -0.11556 | 3.170733363636364 | 2.2196927272727276 | -1.4498047272727272 | 21.505107769600002 | 2.2317073320999996 | 1.3354113599999998e-2 | 4.873414533497023 | 4.965098592601861 | 1.4666266363636362 | 3.7135827272727275 | 1.3342447272727274 | 2.0088075950413224 | 1.669623991735537 | 2.252981991735537 | 2.1509936904913136 | 13.790696672298349 | 1.7802089922550746 | 4.605097499204145 | 3.8262273719452935 | 5.685248493236532 | 2.1459490905434233 | 1.9560744801630876 | 2.3843759127361883 |
| user_001 | Jogging | 1.446047146878e12 | 7.5497 | 4.59904 | -2.03158 | 9.601578181818182 | -2.3648095454545466 | -2.6621189090909088 | 56.997970089999995 | 21.151168921599997 | 4.127317296399999 | 9.070637039811482 | 12.266509876796164 | 2.051878181818182 | 6.963849545454546 | 0.6305389090909088 | 2.7384758677685945 | 7.875615661157027 | 2.8087830991735534 | 4.210204073021489 | 48.49520049172749 | 0.39757931587755335 | 12.98708863009436 | 65.10949435063044 | 14.582447447172575 | 3.6037603458185674 | 8.069045442345113 | 3.8186970876429274 |
| user_001 | Jogging | 1.446047146894e12 | 4.59904 | 5.32712 | -2.4531 | 9.615512727272728 | 0.519665 | -3.080158818181818 | 21.151168921599997 | 28.378207494399998 | 6.01769961 | 7.452991079157414 | 11.528459558044117 | 5.0164727272727285 | 4.807455 | 0.6270588181818177 | 3.450409504132231 | 7.804042355371902 | 2.4350809421487605 | 25.16499862347109 | 23.111623577025 | 0.39320276145957794 | 16.347791421865587 | 64.35447876626505 | 13.496922844239998 | 4.043240213228196 | 8.022124330017894 | 3.673815842450462 |
| user_001 | Jogging | 1.446047146959e12 | -0.535886 | 0.690364 | 0.689167 | 2.303301818181818 | 4.846375818181818 | -1.5159943636363635 | 0.287173804996 | 0.476602452496 | 0.47495115388899994 | 1.1129813167259368 | 6.126109555918774 | 2.839187818181818 | 4.156011818181819 | 2.2051613636363636 | 4.69154547107438 | 3.318032727272727 | 1.1686108760330578 | 8.060987466912033 | 17.272434232866946 | 4.862736639674587 | 23.731583464890104 | 15.101918680113487 | 1.6301781262528727 | 4.8715073093335395 | 3.886118716677797 | 1.2767842911991332 |
| user_001 | Jogging | 1.446047146983e12 | 3.37279 | -5.51753 | 3.44823 | 1.1153718181818182 | 2.536705818181819 | -0.11904436363636352 | 11.375712384100002 | 30.4431373009 | 11.8902901329 | 7.328651978222189 | 5.187779500312943 | 2.257418181818182 | 8.05423581818182 | 3.5672743636363635 | 4.118008198347107 | 3.6100269173553725 | 1.9562364214876033 | 5.095936847603308 | 64.87071461488296 | 12.725446385457222 | 20.640781581717523 | 18.8476167174582 | 4.691012272734492 | 4.543212693867361 | 4.341384193717276 | 2.1658744822206324 |
| user_001 | Jogging | 1.446047147096e12 | 11.42005 | -19.58108 | -3.0279 | 7.344163636363636 | -19.22574545454545 | -2.8397851818181823 | 130.4175420025 | 383.4186939664 | 9.16817841 | 22.869289765510864 | 20.970475507049734 | 4.075886363636364 | 0.35533454545455 | 0.18811481818181752 | 2.502220925619835 | 5.999820628099176 | 2.605780305785124 | 16.612849649276864 | 0.12626263919339167 | 3.538718481957826e-2 | 8.992153747846151 | 54.692642601319115 | 8.427556686649925 | 2.9986920061663804 | 7.395447424011553 | 2.903025436790027 |
| user_001 | Jogging | 1.446047147137e12 | 7.43474 | -17.39682 | 0.459245 | 8.991937272727275 | -19.361609090909088 | -2.7039215454545453 | 55.2753588676 | 302.64934611240005 | 0.210905970025 | 18.924471219826067 | 21.673241495557797 | 1.5571972727272758 | 1.9647890909090862 | 3.1631665454545455 | 2.383775785123967 | 1.5410471074380183 | 2.245062066115702 | 2.424863346189266 | 3.860396171755353 | 10.005622594282842 | 8.250315360901206 | 5.332303207768298 | 7.447613372994453 | 2.8723362200308666 | 2.3091780372609425 | 2.729031581531158 |
| user_001 | Jogging | 1.446047147291e12 | -5.096 | 5.40376 | 0.191003 | -2.6330516363636365 | 0.955122909090909 | 3.1277306363636366 | 25.969216 | 29.2006221376 | 3.6482146009e-2 | 7.430095577017095 | 5.745923703540493 | 2.4629483636363636 | 4.4486370909090915 | 2.9367276363636368 | 3.6375790661157024 | 4.76628705785124 | 2.561125983471075 | 6.066114641939041 | 19.790371966612106 | 8.624369210181953 | 13.51211838325738 | 23.052495135020134 | 7.287400355275503 | 3.675883347340797 | 4.801301400143521 | 2.6995185413839082 |
| user_001 | Jogging | 1.446047147299e12 | -5.13432 | 6.40009 | -2.87462 | -3.1730193636363637 | 1.8434574545454545 | 2.7096906363636366 | 26.361241862399996 | 40.961152008099994 | 8.2634401444 | 8.694011388013015 | 6.184343097734112 | 1.961300636363636 | 4.556632545454545 | 5.584310636363637 | 3.455794826446281 | 4.613322495867768 | 2.9215261900826452 | 3.8467001862004038 | 20.76290015429557 | 31.184525283404046 | 12.435550685654261 | 21.401108802699216 | 9.883802371350505 | 3.5264076176265076 | 4.626133245238319 | 3.143851518655184 |
| user_001 | Jogging | 1.446047147355e12 | 3.41111 | 0.881966 | -3.83263 | -2.8525222727272723 | 4.790637818181818 | -3.0313875454545456 | 11.635671432099999 | 0.7778640251560001 | 14.6890527169 | 5.2060146152461 | 7.462339132871702 | 6.263632272727272 | 3.9086718181818174 | 0.8012424545454544 | 2.6555029669421484 | 3.10837867768595 | 3.824113 | 39.233089247950616 | 15.277715382248754 | 0.6419894709660245 | 10.58537307420908 | 12.414674257063336 | 17.85669599316412 | 3.2535170314920867 | 3.523446360747292 | 4.225718399652788 |
| user_001 | Jogging | 1.446047147736e12 | -2.18366 | 1.87829 | -4.98224 | 6.058691818181817 | 2.9164280909090907 | -5.292285454545453 | 4.768370995600001 | 3.5279733241 | 24.8227154176 | 5.754916136426317 | 9.230739311965165 | 8.242351818181817 | 1.0381380909090907 | 0.3100454545454534 | 4.265905685950414 | 4.891699719008265 | 1.823541669421487 | 67.9363634946851 | 1.0777306957963715 | 9.612818388429681e-2 | 25.483026403005535 | 31.63158155332584 | 5.8424057463163965 | 5.0480715528809155 | 5.624196080625731 | 2.4171068959225606 |
| user_001 | Jogging | 1.446047147743e12 | -4.59784 | 1.53341 | -4.75232 | 5.348024545454545 | 3.146349818181818 | -5.281834545454545 | 21.140132665599996 | 2.3513462280999997 | 22.5845453824 | 6.787932253352268 | 9.309637091395926 | 9.945864545454544 | 1.6129398181818182 | 0.5295145454545445 | 5.135555644628099 | 4.338747471074381 | 1.5869687272727269 | 98.92022155652973 | 2.601574857076397 | 0.28038565384793285 | 34.462666228033235 | 26.484506083387938 | 4.976233791932779 | 5.8704911402738045 | 5.146309948243299 | 2.230747361745112 |
| user_001 | Jogging | 1.446047147849e12 | 7.1665 | -18.81467 | -3.64103 | 1.3661955454545456 | -7.579855363636364 | 1.4277029999999997 | 51.35872225 | 353.99180720889996 | 13.257099460900001 | 20.459902954799173 | 9.189368471935754 | 5.800304454545454 | 11.234814636363636 | 5.068733 | 3.350967495867769 | 6.293398710743801 | 3.3427343553719013 | 33.64353176541984 | 126.22105991345057 | 25.692054225289 | 13.470611529964833 | 48.82826791895325 | 16.2002231833327 | 3.6702331710621374 | 6.9877226561271915 | 4.02495008457654 |
| user_001 | Jogging | 1.446047148067e12 | -3.63983 | 5.86361 | 1.37893 | -1.493894 | -1.0688853636363633 | 1.0514679090909091 | 13.248362428899998 | 34.3819222321 | 1.9014479449 | 7.037878416532926 | 5.108012830374895 | 2.145936 | 6.932495363636364 | 0.32746209090909084 | 5.126688247933884 | 7.607690545454546 | 0.7584883636363636 | 4.605041316095999 | 48.05949196683968 | 0.10723142098255367 | 29.09375933065228 | 58.08169441570896 | 0.6899635360873132 | 5.393863117530169 | 7.621134719692925 | 0.8306404373056451 |
| user_001 | Jogging | 1.446047148456e12 | 15.482 | -4.44456 | -14.83056 | 5.260932727272727 | -12.206161818181817 | -1.9409999090909091 | 239.69232399999999 | 19.7541135936 | 219.9455099136 | 21.895021066607814 | 16.00252889430085 | 10.221067272727272 | 7.761601818181817 | 12.889560090909091 | 3.934323636363636 | 4.666527107438015 | 4.5835542479338836 | 104.47021619361651 | 60.24246278400329 | 166.14075933715637 | 22.934490962878886 | 27.256494774350404 | 45.20788764888824 | 4.7889968639454015 | 5.220775303951551 | 6.723681108506577 |
| user_001 | Jogging | 1.446047148618e12 | 6.40009 | -14.40784 | 4.44456 | 6.250292727272727 | -4.280825272727273 | -0.39425281818181795 | 40.961152008099994 | 207.5858534656 | 19.7541135936 | 16.37989984912301 | 10.027533534700176 | 0.14979727272727228 | 10.127014727272726 | 4.838812818181818 | 1.954652314049587 | 4.081905495867769 | 4.160129082644629 | 2.2439222916528792e-2 | 102.55642728639869 | 23.41410948940067 | 7.110492361411645 | 26.382294292424103 | 21.12838448437969 | 2.66655064857423 | 5.136369758148658 | 4.596562246329282 |
| user_001 | Jogging | 1.446047148715e12 | 13.25943 | -19.58108 | 4.09967 | 11.325992727272727 | -19.563661818181814 | 3.6816355454545455 | 175.8124839249 | 383.4186939664 | 16.8072941089 | 24.00080148662123 | 23.3560518559995 | 1.9334372727272733 | 1.741818181818644e-2 | 0.4180344545454542 | 3.472263223140496 | 4.51767934710744 | 3.3148654049586783 | 3.7381796875710767 | 3.033930578514007e-4 | 0.1747528051871154 | 15.492212397006087 | 35.18123736599198 | 16.896627558972657 | 3.9360147861772683 | 5.931377358252633 | 4.11055076102615 |
| user_001 | Jogging | 1.446047148869e12 | -12.72175 | 2.0699 | -3.06622 | -7.5937909090909095 | -2.7375616363636364 | -0.14342963636363623 | 161.8429230625 | 4.28448601 | 9.4017050884 | 13.248740097114895 | 10.998473902827046 | 5.127959090909091 | 4.807461636363636 | 2.9227903636363637 | 7.836028842975208 | 7.575704776859505 | 3.0896938347107437 | 26.29596443803719 | 23.111687385108134 | 8.542703509765587 | 66.6865304416707 | 59.766724667649676 | 12.372987812976406 | 8.166182121510069 | 7.730894169994159 | 3.517525808430751 |
| user_001 | Jogging | 1.446047148941e12 | -5.70913 | 1.41845 | -2.2615 | -8.172079090909092 | 1.7528828181818183 | -1.1432400909090907 | 32.5941653569 | 2.0120004025 | 5.114382249999999 | 6.3024239788671785 | 10.5019898639832 | 2.462949090909092 | 0.33443281818181836 | 1.118259909090909 | 5.765783223140496 | 3.3173993801652886 | 1.9834706446280992 | 6.066118224409922 | 0.11184530987703317 | 1.2505052242800083 | 60.86196647578083 | 18.08946040022318 | 4.829903946091108 | 7.80140798034437 | 4.253170629098153 | 2.1977042444539956 |
| user_001 | Jogging | 1.446047148948e12 | -3.29495 | 1.38013 | -5.13552 | -7.311613636363638 | 1.8643600909090912 | -1.5090246363636362 | 10.856695502500001 | 1.9047588169000003 | 26.373565670399998 | 6.255798908996356 | 9.906212785151986 | 4.016663636363638 | 0.4842300909090911 | 3.6264953636363635 | 5.211563223140495 | 2.7061745785123965 | 2.059477876033058 | 16.133586767685962 | 0.23447878094182661 | 13.15146862247604 | 53.03097794228993 | 13.387960177970843 | 5.317635599192631 | 7.282237152296671 | 3.658956159613127 | 2.305999913094671 |
| user_001 | Jogging | 1.446047149094e12 | 7.7413 | -19.58108 | 8.19995 | 7.0828891818181825 | -12.133004545454547 | 5.524493636363636 | 59.927725689999995 | 383.4186939664 | 67.23918000249999 | 22.596141255951203 | 19.778463287097466 | 0.6584108181818173 | 7.448075454545453 | 2.6754563636363633 | 3.3142304132231404 | 6.506534396694215 | 10.932690958677686 | 0.4335048054988501 | 55.47382797660246 | 7.158066753722312 | 18.004113796689182 | 51.223312981827924 | 143.1732729431186 | 4.243125475011219 | 7.157046386731605 | 11.965503455480619 |
| user_001 | Jogging | 1.446047149143e12 | 11.38173 | -19.58108 | 18.04827 | 7.53924909090909 | -18.473275454545455 | 6.872672454545454 | 129.5437777929 | 383.4186939664 | 325.74004999289997 | 28.960361215844667 | 23.99309396780716 | 3.8424809090909093 | 1.1078045454545453 | 11.175597545454544 | 3.264192983471074 | 5.7432970495867774 | 8.856425851239669 | 14.764659536728102 | 1.2272309109297517 | 124.89398049796962 | 17.534738231656146 | 44.04750329577925 | 115.42028427408805 | 4.187450087064459 | 6.636829310429737 | 10.743383278748276 |
| user_001 | Jogging | 1.446047149151e12 | 10.61533 | -19.58108 | 8.31491 | 7.037601818181818 | -19.163040909090906 | 6.83783609090909 | 112.6852310089 | 383.4186939664 | 69.1377283081 | 23.77481131961724 | 24.163151142198014 | 3.5777281818181823 | 0.41803909090909386 | 1.477073909090909 | 2.891441256198348 | 5.236898677685951 | 8.162859975206612 | 12.800138942976037 | 0.17475668152810164 | 2.1817473329170993 | 13.339147160653248 | 40.80328224402284 | 108.08001828626712 | 3.6522797210308586 | 6.387744691518505 | 10.39615401416635 |
| user_001 | Jogging | 1.446047149159e12 | 9.27412 | -19.58108 | 2.95007 | 7.236170909090908 | -19.23271454545454 | 5.32592609090909 | 86.0093017744 | 383.4186939664 | 8.702913004900001 | 21.866204717456114 | 23.59957183787027 | 2.037949090909092 | 0.3483654545454584 | 2.37585609090909 | 2.9997523223140505 | 4.254188760330579 | 6.8035970247933895 | 4.153236497137195 | 0.12135848992066385 | 5.644692164709821 | 13.651567880630706 | 29.495691271576558 | 81.29763851446782 | 3.694802820263986 | 5.430993580513289 | 9.016520310766666 |
| user_001 | Jogging | 1.446047149523e12 | 12.79958 | -19.58108 | -1.99325 | 15.234664545454546 | -19.581079999999996 | 1.622790272727273 | 163.82924817640003 | 383.4186939664 | 3.9730455625 | 23.478095913112288 | 25.618920731320905 | 2.4350845454545453 | 3.552713678800501e-15 | 3.6160402727272727 | 5.13745561983471 | 3.858000157024797 | 4.932868305785124 | 5.92963674351157 | 1.2621774483536189e-29 | 13.07574725398553 | 33.41003256049624 | 28.645617400991785 | 37.17574199616442 | 5.780141223231163 | 5.352160068700467 | 6.097191320285465 |
| user_001 | Jogging | 1.446047149563e12 | 3.98591 | -15.74905 | 2.95007 | 11.92169909090909 | -19.128203636363637 | 0.5080178181818181 | 15.8874785281 | 248.0325759025 | 8.702913004900001 | 16.511298175355567 | 23.144901652304863 | 7.93578909090909 | 3.379153636363636 | 2.442052181818182 | 5.774967107438017 | 0.8791501652892587 | 3.5599889008264465 | 62.97674849539172 | 11.418679298149584 | 5.963618858722944 | 41.11842897361307 | 2.1366249748332855 | 16.233832526013103 | 6.412365318165604 | 1.4617198687960993 | 4.029123046769992 |
| user_001 | Jogging | 1.446047149685e12 | -8.96635 | 0.307161 | 0.919089 | -6.893574181818181 | -2.5215752727272727 | -4.082363636363617e-3 | 80.3954323225 | 9.434787992100001e-2 | 0.8447245899210001 | 9.018564452968223 | 8.545217563577793 | 2.072775818181819 | 2.828736272727273 | 0.9231713636363637 | 5.534592785123967 | 5.788583925619835 | 2.250131214876033 | 4.29639959243931 | 8.001748900642985 | 0.8522453666382231 | 33.3850424263692 | 36.61460580992789 | 7.356495611869291 | 5.77797909535585 | 6.051000397448995 | 2.712286049049637 |
| user_001 | Jogging | 1.44604714979e12 | 12.2631 | 1.03525 | -12.87622 | -0.8424509090909097 | 1.226849090909091 | -4.585100454545454 | 150.38362161 | 1.0717425625 | 165.7970414884 | 17.811580661493803 | 8.372394787765712 | 13.10555090909091 | 0.191599090909091 | 8.291119545454546 | 4.821074049586776 | 0.8218279752066114 | 4.478725966942149 | 171.7554646307736 | 3.671021163719011e-2 | 68.7426633170184 | 36.154911444741394 | 0.9207269402372962 | 42.2056841224175 | 6.012895429386861 | 0.9595451736303487 | 6.496590191971285 |
| user_001 | Jogging | 1.446047149823e12 | 6.47673 | -5.51753 | 3.25663 | 5.142486363636363 | 1.4533363636363655e-2 | -5.609298181818182 | 41.9480314929 | 30.4431373009 | 10.6056389569 | 9.110258380018648 | 11.598371733840915 | 1.3342436363636372 | 5.532063363636364 | 8.865928181818182 | 6.808348099173553 | 1.6062858842975205 | 6.176854107438017 | 1.7802060811768616 | 30.60372505928768 | 78.60468252515786 | 68.21052244459113 | 5.486770549778637 | 54.33045585514098 | 8.258966184976854 | 2.3423856535119567 | 7.37091960715493 |
| user_001 | Jogging | 1.446047149846e12 | -7.6042e-2 | -8.54483 | -3.18118 | 6.117913545454544 | -3.085928 | -3.477294181818182 | 5.782385764e-3 | 73.01411972889998 | 10.1199061924 | 9.118103328382716 | 14.170774292178116 | 6.1939555454545445 | 5.458901999999999 | 0.29611418181818205 | 8.038082157024792 | 3.8665507438016533 | 8.655006363636364 | 38.365085299067104 | 29.799611045603992 | 8.768360867385137e-2 | 78.59189304798379 | 26.740223951981918 | 99.63844021386738 | 8.865206881285049 | 5.171095043797002 | 9.981905640400903 |
| user_001 | Jogging | 1.446047149871e12 | 11.15181 | -15.21256 | 16.7837 | 8.31958990909091 | -6.768160727272726 | -0.6938469090909084 | 124.36286627609998 | 231.4219817536 | 281.69258569 | 25.248315463010595 | 16.46430774422488 | 2.8322200909090895 | 8.444399272727274 | 17.477546909090908 | 7.11934505785124 | 5.128903884297521 | 8.724047438016528 | 8.021470643349092 | 71.30787907723692 | 305.46464595947316 | 68.9577918733565 | 37.06629125091457 | 114.16631974726042 | 8.304082843599074 | 6.088209199010376 | 10.684864049077106 |
| user_001 | Jogging | 1.446047149944e12 | 9.38908 | -19.58108 | 8.58315 | 6.636980909090909 | -18.389666363636362 | 5.719578181818182 | 88.1548232464 | 383.4186939664 | 73.67046392249999 | 23.350459976953342 | 22.55196135090203 | 2.752099090909091 | 1.191413636363638 | 2.8635718181818177 | 2.407844991735537 | 5.750263537190083 | 6.912224115702478 | 7.574049406182646 | 1.4194664529132273 | 8.200043557885122 | 9.657153846656074 | 41.139247568377066 | 86.3043109583581 | 3.1075961524393856 | 6.413988429080385 | 9.290011354048934 |
| user_001 | Jogging | 1.446047149992e12 | 4.33079 | -17.70339 | 2.18366 | 7.061987272727273 | -19.392962727272728 | 3.0963790909090907 | 18.7557420241 | 313.41001749209994 | 4.768370995600001 | 18.355765593180795 | 21.769817840277774 | 2.7311972727272726 | 1.6895727272727292 | 0.9127190909090905 | 2.983601123966942 | 1.6176867768595058 | 2.8933422314049584 | 7.459438542552892 | 2.8546560007438084 | 0.8330561389099167 | 11.920819932043132 | 5.32959220910083 | 22.634681774097146 | 3.45265404175442 | 2.3085909575108428 | 4.75759201425439 |
| user_001 | Jogging | 1.446047150009e12 | 5.86361 | -14.56112 | 3.29495 | 7.765688181818183 | -18.612621818181818 | 4.002131818181819 | 34.3819222321 | 212.02621565440003 | 10.856695502500001 | 16.03947734151584 | 20.98445602921786 | 1.9020781818181822 | 4.051501818181817 | 0.7071818181818186 | 2.7292931239669422 | 1.4770734710743818 | 1.7041457024793387 | 3.617901409748762 | 16.414666982730573 | 0.5001061239669428 | 10.436125994546856 | 4.233277587820513 | 7.70068862804275 | 3.230499341363012 | 2.057493034695504 | 2.775011464488525 |
| user_001 | Jogging | 1.446047150024e12 | 7.1665 | -11.72542 | 2.91174 | 7.898067272727272 | -17.313215454545453 | 4.615257272727273 | 51.35872225 | 137.4854741764 | 8.4782298276 | 14.04715011146389 | 19.75562821218464 | 0.731567272727272 | 5.587795454545454 | 1.7035172727272734 | 2.720109256198347 | 1.9172815702479349 | 1.0900706611570252 | 0.5351906745256187 | 31.223458041838832 | 2.9019710984801677 | 10.436761381613074 | 7.754035861739667 | 1.7250466609229904 | 3.2305976817940474 | 2.784606949237121 | 1.3134103170460443 |
| user_001 | Jogging | 1.446047150089e12 | 11.42005 | 0.920286 | -10.76861 | 10.235605454545455 | -6.9284110000000005 | -5.013593999999999 | 130.4175420025 | 0.8469263217960001 | 115.96296133210001 | 15.72346748196453 | 16.668982547881402 | 1.1844445454545447 | 7.8486970000000005 | 5.755016000000001 | 2.9617473553719007 | 6.841284181818182 | 6.333937190082644 | 1.402908881257023 | 61.602044597809005 | 33.12020916025602 | 17.983586891344775 | 50.480642150793095 | 79.11948582376384 | 4.2407059425695595 | 7.104973057710571 | 8.894913480397875 |
| user_001 | Jogging | 1.4460471503e12 | 11.80326 | -19.58108 | -4.86728 | 9.876788181818181 | -16.619965454545454 | 5.089035181818182 | 139.3169466276 | 383.4186939664 | 23.6904145984 | 23.37575785279271 | 20.51629860347898 | 1.9264718181818186 | 2.9611145454545458 | 9.956315181818182 | 1.6841933057851242 | 8.302206694214878 | 7.953840752066115 | 3.711293666248762 | 8.768199351302481 | 99.12821199970321 | 4.3451937504936895 | 76.95784186172195 | 71.04635112082377 | 2.0845128328925417 | 8.772561875628005 | 8.428899757431202 |
| user_001 | Jogging | 1.446047150316e12 | 13.02951 | -19.58108 | 3.44823 | 10.841763636363636 | -18.375732727272727 | 5.618552636363637 | 169.7681308401 | 383.4186939664 | 11.8902901329 | 23.771350717605426 | 22.540432916645994 | 2.1877463636363643 | 1.2053472727272734 | 2.1703226363636365 | 1.940401487603306 | 6.922042231404959 | 7.5085643553719015 | 4.786234151604135 | 1.4528620478710759 | 4.7103003459124055 | 5.182578915665215 | 62.486530982975154 | 68.06526032762473 | 2.2765278200947194 | 7.9048422490885395 | 8.25016729088718 |
| user_001 | Jogging | 1.446047150381e12 | 5.02056 | -16.13225 | 5.90073 | 9.730475454545456 | -18.960987272727273 | 3.984714545454546 | 25.206022713599996 | 260.24949006249994 | 34.8186145329 | 17.89620427098998 | 21.991917768767674 | 4.709915454545456 | 2.828737272727274 | 1.9160154545454544 | 2.4594676033057854 | 1.1188903305785145 | 1.9137989752066118 | 22.18330358896613 | 8.001754558116536 | 3.6711152220570242 | 7.882300682133286 | 2.303614757833587 | 10.607043927891384 | 2.807543531654191 | 1.5177663712948666 | 3.2568457021927495 |
| user_001 | Jogging | 1.446047150388e12 | 3.94759 | -14.82936 | 5.70913 | 9.016323636363637 | -18.52901272727273 | 4.946206363636364 | 15.583466808099999 | 219.90991800959998 | 32.5941653569 | 16.373379314442086 | 21.35533790164489 | 5.068733636363637 | 3.6996527272727295 | 0.7629236363636362 | 2.7451277685950415 | 1.1860301652892584 | 1.0780361074380165 | 25.692060676404143 | 13.687430302416546 | 0.5820524749223138 | 9.880552228511048 | 2.7508175715712295 | 1.6483021529113067 | 3.1433345715197176 | 1.6585588839625893 | 1.2838622016833843 |
| user_001 | Jogging | 1.446047150446e12 | -4.7128 | -3.67815 | -0.728685 | 1.3069736363636362 | -10.878886363636362 | 2.3439079090909094 | 22.21048384 | 13.5287874225 | 0.530981829225 | 6.022478982256808 | 11.966450375373627 | 6.019773636363636 | 7.200736363636361 | 3.0725929090909094 | 5.286303041322314 | 5.421531652892561 | 2.5579606198347107 | 36.237674633058674 | 51.850604178595006 | 9.440827184995738 | 28.597294023410033 | 36.216624051849195 | 7.94891358854585 | 5.3476437823970695 | 6.018024929480535 | 2.819381774174234 |
| user_001 | Jogging | 1.44604715051e12 | -5.21096 | 0.498763 | -1.34181 | -6.489467272727273 | -1.9398028181818185 | -1.4532865454545456 | 27.1541041216 | 0.24876453016900002 | 1.8004540760999999 | 5.404009874886333 | 7.411970523117133 | 1.278507272727273 | 2.4385658181818184 | 0.11147654545454566 | 4.6402411735537195 | 5.203328504132231 | 2.136752809917356 | 1.6345808464165297 | 5.946603249604761 | 1.2427020186479384e-2 | 26.512593572034266 | 31.3340397920144 | 6.342812250172631 | 5.149038121050792 | 5.59768164439658 | 2.5184940441010837 |
| user_001 | Jogging | 1.446047150519e12 | -4.40624 | 0.230521 | -1.03525 | -6.698487272727272 | -1.4172527272727273 | -1.543862090909091 | 19.414950937600004 | 5.3139931441e-2 | 1.0717425625 | 4.5320893009230305 | 7.287043773994804 | 2.2922472727272716 | 1.6477737272727273 | 0.508612090909091 | 4.3330447272727275 | 4.609839090909092 | 1.8814949421487606 | 5.254397559325614 | 2.7151582562902563 | 0.25868625901891745 | 24.06618691699167 | 25.50364074727978 | 5.366435404843603 | 4.90573000857076 | 5.050112944012221 | 2.3165567993994025 |
| user_001 | Jogging | 1.446047150535e12 | -3.98471 | 1.15021 | -0.767005 | -6.6810681818181825 | -0.518467 | -1.5264437272727276 | 15.877913784100002 | 1.3229830441 | 0.5882966700250001 | 4.217723734222643 | 6.9728435823196095 | 2.6963581818181823 | 1.668677 | 0.7594387272727275 | 3.7319544793388433 | 3.533386834710744 | 1.4596547520661156 | 7.270347444657854 | 2.7844829303289997 | 0.5767471804816202 | 18.843668655735463 | 15.710387972942828 | 3.602534299062354 | 4.340929469103992 | 3.9636331784037266 | 1.8980343250485103 |
| user_001 | Jogging | 1.446047150656e12 | 5.13552 | -11.95534 | 4.78944 | 7.013215454545456 | -3.5074520909090907 | 1.0270821818181823 | 26.373565670399998 | 142.93015451559998 | 22.938735513599998 | 13.865152566762472 | 12.758929470610324 | 1.8776954545454565 | 8.447887909090909 | 3.7623578181818176 | 6.062842892561983 | 3.6736822892561984 | 7.44934294214876 | 3.5257402200206687 | 71.36681012456437 | 14.155336352033848 | 49.57596519016131 | 20.885922032531145 | 78.13713376841855 | 7.041020181064766 | 4.5701118183837846 | 8.839521127777147 |
| user_001 | Jogging | 1.446047150704e12 | 3.8919e-2 | -19.38948 | 8.08499 | 6.577758090909092 | -11.634841818181819 | 3.5004854545454545 | 1.514688561e-3 | 375.95193467039996 | 65.36706330009999 | 21.00762986771856 | 16.979426057591795 | 6.538839090909092 | 7.75463818181818 | 4.5845045454545446 | 3.814614214876033 | 6.555623876033058 | 8.284472793388431 | 42.756416656800845 | 60.134413330912366 | 21.01768192729338 | 18.63662567338302 | 46.999266380506135 | 106.19925324881555 | 4.317015829642395 | 6.855601095491637 | 10.305302191047847 |
| user_001 | Jogging | 1.446047150729e12 | 6.28513 | -19.58108 | 7.5485 | 6.539437181818181 | -15.44248727272727 | 3.598028181818181 | 39.5028591169 | 383.4186939664 | 56.97985224999999 | 21.9066520795237 | 19.515162785692446 | 0.2543071818181817 | 4.13859272727273 | 3.9504718181818186 | 3.140049247933885 | 6.547072876033059 | 6.394109173553718 | 6.467214272430571e-2 | 17.127949762234735 | 15.606227586248764 | 14.839014316679823 | 46.59829215845338 | 70.57621021913586 | 3.8521441194067263 | 6.826294174620179 | 8.400964838584665 |
| user_001 | Jogging | 1.446047150793e12 | 10.46204 | -19.54276 | 6.24561 | 7.661177272727273 | -19.535792727272725 | 5.656872090909091 | 109.4542809616 | 381.91946841760006 | 39.0076442721 | 23.030010717568068 | 22.099586635479316 | 2.800862727272727 | 6.967272727276708e-3 | 0.5887379090909093 | 2.9281772727272735 | 2.3704752066115735 | 2.3156854628099173 | 7.844832017025618 | 4.8542889256253815e-5 | 0.34661232560073574 | 12.87407932845424 | 9.62765954508025 | 7.460318108028584 | 3.588046728856 | 3.102847006392718 | 2.731358289940846 |
| user_001 | Jogging | 1.446047150899e12 | 10.96021 | 1.95493 | -8.08618 | 10.18335090909091 | -6.249096636363637 | -2.3416207272727267 | 120.1262032441 | 3.8217513049000003 | 65.38630699240001 | 13.759878689196356 | 14.293301046915387 | 0.77685909090909 | 8.204026636363636 | 5.744559272727274 | 1.2078809090909093 | 7.259007033057852 | 4.546814446280991 | 0.6035100471280977 | 67.30605305016404 | 32.99996123787691 | 2.1495073227730286 | 55.00847255568874 | 31.641875632767004 | 1.4661198186959443 | 7.4167696846867734 | 5.625111166258583 |
| user_001 | Jogging | 1.446047150955e12 | 4.25415 | 3.71767 | 3.25663 | 9.152185454545453 | 2.581994272727273 | -3.8883669090909097 | 18.0977922225 | 13.8210702289 | 10.6056389569 | 6.521081306677598 | 11.345821028162165 | 4.898035454545453 | 1.135675727272727 | 7.14499690909091 | 2.3831438842975206 | 6.168620760330578 | 5.639735181818182 | 23.990751313984283 | 1.2897593575164372 | 51.05098083091866 | 7.891078996077311 | 45.31626243044999 | 40.12753928982287 | 2.809106440859319 | 6.731735469435054 | 6.334630162039681 |
| user_001 | Jogging | 1.446047150995e12 | 4.9056 | -0.497565 | -2.0699 | 5.626716363636364 | 3.031387181818182 | 0.48711390909090907 | 24.064911359999996 | 0.24757092922499999 | 4.28448601 | 5.347613327384937 | 6.885097826662445 | 0.721116363636364 | 3.5289521818181817 | 2.557013909090909 | 2.9680827272727273 | 3.1476498347107436 | 3.661964247933885 | 0.5200088099041328 | 12.453503501559306 | 6.538320131284372 | 11.189279831239894 | 14.854897650394623 | 17.366285791314564 | 3.345038091149321 | 3.854205190489295 | 4.167287582026775 |
| user_001 | Jogging | 1.446047151012e12 | 3.56439 | -1.34061 | -12.8379 | 4.762767272727273 | 2.0176410909090907 | -1.3174242727272727 | 12.7048760721 | 1.7972351721000002 | 164.81167641 | 13.390809820701659 | 7.32252464352399 | 1.198377272727273 | 3.358251090909091 | 11.520475727272727 | 2.8512215702479344 | 2.50602194214876 | 5.361042347107439 | 1.436108087789257 | 11.2778503895921 | 132.72136098268007 | 10.987531009836438 | 8.24752372582097 | 39.37473826934931 | 3.314744486357348 | 2.8718502269131254 | 6.274929343773467 |
| user_001 | Jogging | 1.446047151093e12 | 3.60271 | -19.58108 | 5.21096 | 4.881213636363636 | -10.58974181818182 | -0.8331956363636361 | 12.9795193441 | 383.4186939664 | 27.1541041216 | 20.580386717263114 | 13.800648451231167 | 1.2785036363636362 | 8.99133818181818 | 6.044155636363636 | 1.3541947107438017 | 7.364467082644628 | 5.066515809917355 | 1.634571548195041 | 80.84416229982146 | 36.531817356586316 | 2.329763991264012 | 62.92199581348555 | 32.83166793913865 | 1.5263564430577845 | 7.932338609356358 | 5.729892489317636 |
| user_001 | Jogging | 1.446047151126e12 | 10.73029 | -19.50444 | -6.93658 | 6.086562727272726 | -16.62344909090909 | 2.361324818181818 | 115.1391234841 | 380.42317971359995 | 48.116142096400004 | 23.316913288299975 | 18.38471454402584 | 4.643727272727274 | 2.880990909090908 | 9.297904818181818 | 1.5318631404958678 | 7.782823462809918 | 4.8175921652892555 | 21.564202983471084 | 8.300108618264456 | 86.45103400796867 | 3.852389123358829 | 67.63887646110612 | 27.84266451956603 | 1.9627503976203466 | 8.22428577209633 | 5.276614873151728 |
| user_001 | Jogging | 1.446047151133e12 | 14.60064 | -17.43514 | -6.17017 | 6.818132727272726 | -17.577972727272723 | 1.504343 | 213.1786884096 | 303.9841068196 | 38.0709978289 | 23.563399437646936 | 19.610273457596872 | 7.782507272727274 | 0.14283272727272234 | 7.674512999999999 | 2.0284438842975203 | 7.265657661157024 | 4.980373958677685 | 60.56741945005292 | 2.040118798016388e-2 | 58.89814978716899 | 8.869158837363564 | 64.54907492690951 | 30.04972946376538 | 2.978113301633026 | 8.034243892670268 | 5.481763353499071 |
| user_001 | Jogging | 1.446047151142e12 | 15.67361 | -17.93331 | -1.49509 | 7.608924545454545 | -18.25032 | 1.0235975454545456 | 245.6620504321 | 321.6036075561 | 2.2352941081 | 23.864219075769064 | 20.580314137387138 | 8.064685454545454 | 0.3170099999999998 | 2.5186875454545454 | 2.541492396694215 | 6.550239049586776 | 4.633907570247934 | 65.03915148075703 | 0.10049534009999987 | 6.343786951627843 | 14.2489020513701 | 58.46542353120962 | 26.984019767424993 | 3.774771787985348 | 7.646268601822043 | 5.194614496517041 |
| user_001 | Jogging | 1.446047151215e12 | 1.91661 | -12.91335 | 6.09233 | 7.103791818181819 | -17.584940909090907 | 1.7342665454545454 | 3.6733938920999996 | 166.7546082225 | 37.11648482889999 | 14.406404372483093 | 19.79776161932016 | 5.18718181818182 | 4.671590909090908 | 4.358063454545454 | 4.890749752066115 | 1.391565537190083 | 2.648218975206611 | 26.90685521487605 | 21.82376162190081 | 18.99271707384466 | 28.641556203589325 | 4.338947391800674 | 10.980097076287576 | 5.351780657275607 | 2.083014016227609 | 3.3136229532473327 |
| user_001 | Jogging | 1.446047151546e12 | 5.51872 | -19.58108 | 1.76214 | 6.473250000000001 | -17.111162727272724 | 2.2359154545454545 | 30.4562704384 | 383.4186939664 | 3.1051373796000004 | 20.420090640944764 | 20.381081270974065 | 0.954530000000001 | 2.469917272727276 | 0.47377545454545444 | 4.775471900826445 | 6.791562008264463 | 4.200666115702479 | 0.9111275209000019 | 6.100491334116545 | 0.22446318132975196 | 31.358268959488054 | 52.18630731844407 | 36.03881805361188 | 5.599845440678524 | 7.224009089033878 | 6.003233966256177 |
| user_001 | Jogging | 1.446047151571e12 | 9.15915 | -19.58108 | 2.79678 | 5.037978181818182 | -19.107301818181814 | 4.193733636363636 | 83.8900287225 | 383.4186939664 | 7.8219783684 | 21.79749299936348 | 20.861238066323242 | 4.121171818181819 | 0.47377818181818654 | 1.3969536363636363 | 4.048650743801653 | 4.932551404958678 | 1.8906804958677683 | 16.984057154976036 | 0.2244657655669466 | 1.9514794621495868 | 24.858159910320957 | 34.60317561232702 | 6.249868547311647 | 4.985795815145357 | 5.882446396893645 | 2.4999737093240895 |
| user_001 | Jogging | 1.446047151578e12 | 9.54236 | -19.58108 | 2.60518 | 4.891664545454546 | -19.56714545454545 | 4.1031581818181815 | 91.0566343696 | 383.4186939664 | 6.7869628323999995 | 21.93769110841886 | 21.159088532938316 | 4.650695454545454 | 1.3934545454549863e-2 | 1.4979781818181817 | 4.304542066115702 | 4.257987305785124 | 1.9438857024793388 | 21.628968210929752 | 1.9417155702491625e-4 | 2.2439386332033053 | 26.518021072216154 | 29.578972161291038 | 6.378130290521789 | 5.1495651342823265 | 5.43865536334957 | 2.5254960484074784 |
| user_001 | Jogging | 1.446047151676e12 | 8.50771 | -3.90807 | -2.10822 | 5.870574545454546 | -11.997141818181818 | 3.173018909090909 | 72.3811294441 | 15.2730111249 | 4.444591568400001 | 9.596808434964199 | 14.334378693583101 | 2.6371354545454535 | 8.089071818181818 | 5.281238909090909 | 1.8349409917355375 | 4.895182727272727 | 2.271032785123967 | 6.954483405620656 | 65.4330828797033 | 27.89148441489574 | 4.643321405980316 | 31.247187169837947 | 7.021995439934475 | 2.154836746944027 | 5.589918350909783 | 2.6499047982775674 |
| user_001 | Jogging | 1.446047151862e12 | 3.10454 | -14.3312 | 2.10702 | 4.99269 | -3.4726144545454547 | -3.4494249999999993 | 9.638168611600001 | 205.38329344000002 | 4.439533280399999 | 14.814215987759866 | 9.083787584658028 | 1.8881499999999996 | 10.858585545454545 | 5.556444999999999 | 1.6449227272727276 | 4.6769781404958675 | 2.5196400413223135 | 3.5651104224999983 | 117.90888004795438 | 30.87408103802499 | 3.093925017922089 | 31.651917087359298 | 9.060915545981608 | 1.7589556611586572 | 5.626003651559364 | 3.010135469705908 |
| user_001 | Jogging | 1.44604715204e12 | 3.56439 | -8.42987 | 2.91174 | 4.8951472727272725 | -16.045159999999996 | 4.535131818181818 | 12.7048760721 | 71.06270821689999 | 8.4782298276 | 9.604468445291493 | 17.488520052165846 | 1.3307572727272725 | 7.615289999999996 | 1.623391818181818 | 3.4561126446281 | 2.750511818181819 | 2.702056834710744 | 1.7709149189165283 | 57.992641784099945 | 2.635400995339669 | 14.522404558036747 | 14.009495671867688 | 8.405549329122879 | 3.810827280005845 | 3.74292608421108 | 2.899232541401755 |
| user_001 | Jogging | 1.446047152169e12 | -4.82776 | 3.60271 | -2.10822 | -5.869378181818181 | 2.3694910000000005 | -1.1014366363636368 | 23.307266617599996 | 12.9795193441 | 4.444591568400001 | 6.3821138763030545 | 6.586683677697039 | 1.0416181818181816 | 1.2332189999999996 | 1.0067833636363634 | 4.140811446280992 | 3.744305983471075 | 1.2370169504132227 | 1.0849684366942143 | 1.520829101960999 | 1.01361274129495 | 21.005204953242714 | 17.416097017278585 | 2.5625126737539987 | 4.5831435667282685 | 4.173259759142556 | 1.600785017968996 |
| user_001 | Jogging | 1.446047152445e12 | 7.89458 | -18.54643 | 3.17999 | -1.3371281818181813 | -2.117469090909091 | -0.697331909090909 | 62.3243933764 | 343.9700657449 | 10.1123364001 | 20.40604801330723 | 11.0639760360617 | 9.231708181818181 | 16.42896090909091 | 3.877321909090909 | 5.280603446280992 | 5.684073314049588 | 2.931029776859504 | 85.22443595424875 | 269.91075655243725 | 15.033625186716371 | 47.08949045960139 | 74.9789026068423 | 15.588315749706124 | 6.862178259095387 | 8.659035893610922 | 3.9482041170266418 |
| user_001 | Jogging | 1.446047152704e12 | 4.48408 | -11.4955 | 2.79678 | 3.9545590909090915 | -4.942721818181818 | -0.791390909090909 | 20.106973446399998 | 132.14652025 | 7.8219783684 | 12.652093584257113 | 12.192673106227097 | 0.5295209090909081 | 6.552778181818182 | 3.588170909090909 | 6.058727438016527 | 6.943893842975206 | 3.8260147603305783 | 0.28039239316446174 | 42.9389019001124 | 12.87497047284628 | 54.759022243994444 | 85.08570595157579 | 20.00260483485849 | 7.399933935110127 | 9.224191344046144 | 4.472427174908328 |
| user_001 | Jogging | 1.446047152769e12 | 11.11349 | -18.16323 | -2.8363 | 5.403763636363636 | -6.9214436363636365 | -0.85758 | 123.50965998010001 | 329.90292403289993 | 8.04459769 | 21.481554452669386 | 13.565349522260403 | 5.709726363636364 | 11.241786363636361 | 1.97872 | 6.483100909090908 | 7.853763603305786 | 3.914372636363637 | 32.60097514760414 | 126.37776064564045 | 3.9153328384000003 | 57.62411376316807 | 96.43633609191028 | 20.26639757095895 | 7.591054851808678 | 9.820200409966708 | 4.501821583643554 |
| user_001 | Jogging | 1.446047153481e12 | 2.03158 | -1.07237 | -5.36544 | 4.700062727272727 | -7.102593090909091 | -0.19220027272727283 | 4.127317296399999 | 1.1499774169 | 28.787946393600006 | 5.8365435924783435 | 11.49641955233915 | 2.6684827272727274 | 6.03022309090909 | 5.173239727272728 | 4.461940330578512 | 7.525349793388429 | 2.330255355371901 | 7.120800065752893 | 36.36359052613318 | 26.76240927583281 | 31.50830018813525 | 63.92713021075553 | 8.885145880885544 | 5.613225470986824 | 7.995444341045439 | 2.9807961823790543 |
| user_001 | Jogging | 1.446047153675e12 | 3.79431 | -14.25456 | 3.86975 | 6.149267272727273 | -9.903460272727271 | -4.082727272727374e-3 | 14.396788376099998 | 203.1924807936 | 14.974965062499999 | 15.25005685996613 | 13.99042620816972 | 2.354957272727273 | 4.351099727272729 | 3.873832727272727 | 3.436792892561984 | 7.7863079669421476 | 3.2394917685950415 | 5.5458237563710755 | 18.932068836672816 | 15.006579998889254 | 17.24848748501127 | 69.499662545278 | 12.679372273498446 | 4.153129842060235 | 8.336645761052702 | 3.5608106202799448 |
| user_001 | Jogging | 1.446047154387e12 | 9.04419 | -18.69972 | 1.14901 | 5.361958363636363 | -7.3150971818181825 | 0.22235518181818173 | 81.7973727561 | 349.6795280784 | 1.3202239801000002 | 20.803776695941533 | 12.021527729316709 | 3.682231636363637 | 11.384622818181818 | 0.9266548181818184 | 3.539086958677686 | 8.338626049586777 | 2.6770378925619838 | 13.558829823837229 | 129.6096367122661 | 0.8586891520595789 | 17.47642587546827 | 87.39473325913166 | 13.816889204735183 | 4.180481536314718 | 9.348515029625382 | 3.717107639648761 |
| user_001 | Jumping | 1.446047227606e12 | 4.33079 | -12.72175 | -3.18118 | 4.33079 | -12.72175 | -3.18118 | 18.7557420241 | 161.8429230625 | 10.1199061924 | 13.810089473967938 | 13.810089473967938 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_001 | Jumping | 1.446047228189e12 | 7.85626 | -19.2362 | 0.804128 | 2.5335707 | -7.617479899999999 | 0.7811372000000001 | 61.720821187599995 | 370.03139044 | 0.646621840384 | 20.79420191947707 | 9.678680677882113 | 5.3226893 | 11.6187201 | 2.2990799999999867e-2 | 1.5381899021031746 | 5.749652194166667 | 1.5458001811904762 | 28.331021384334495 | 134.99465676214402 | 5.285768846399939e-4 | 4.741160399385161 | 58.53491368491284 | 5.359596330907423 | 2.1774205839444893 | 7.6508113089340295 | 2.3150801996707204 |
| user_001 | Jumping | 1.4460472289e12 | -2.10702 | 0.575403 | 1.07237 | 1.9479667272727275 | -6.587010545454547 | 1.2813905454545456 | 4.439533280399999 | 0.331088612409 | 1.1499774169 | 2.433228166389046 | 8.318907723148978 | 4.054986727272727 | 7.162413545454546 | 0.20902054545454551 | 2.2773663388429752 | 6.5217368677685945 | 1.290223049586777 | 16.44291735835798 | 51.30016779611076 | 4.3689588422115726e-2 | 6.809434217026666 | 49.84400629186418 | 2.8670258503345463 | 2.6094892636350635 | 7.060028774152707 | 1.693229414560988 |
| user_001 | Jumping | 1.446047229743e12 | 2.56806 | -1.03405 | 0.267643 | 1.8504245454545454 | -6.945829272727272 | 0.5149825454545455 | 6.5949321636 | 1.0692594024999997 | 7.163277544900001e-2 | 2.7813349926876842 | 8.172391978699084 | 0.7176354545454546 | 5.911779272727272 | 0.24733954545454545 | 1.3551465785123966 | 6.545490115702479 | 0.7524711818181818 | 0.5150006456206613 | 34.949134169447795 | 6.1176850745661156e-2 | 3.0300886047082014 | 52.47526121010706 | 0.9163994449650903 | 1.7407149694042967 | 7.243981033251472 | 0.9572875456022032 |
| user_001 | Jumping | 1.446047229806e12 | 1.41845 | -0.305964 | 0.727487 | 1.7424309090909091 | -6.952796636363636 | 0.38608681818181817 | 2.0120004025 | 9.361396929600001e-2 | 0.529237335169 | 1.6232226301296442 | 8.01236384525454 | 0.32398090909090915 | 6.646832636363635 | 0.3414001818181818 | 1.3364614049586778 | 6.627197917355372 | 0.7062334793388431 | 0.10496362945537194 | 44.18038409582876 | 0.11655408414548761 | 3.014140832861232 | 53.488019562090564 | 0.8613111484622411 | 1.7361281153363168 | 7.313550407434857 | 0.9280685041861086 |
| user_001 | Jumping | 1.446047230842e12 | 1.18853 | 1.41845 | -2.22318 | 2.264980181818182 | -5.869376909090908 | 0.3895704545454545 | 1.4126035609000003 | 2.0120004025 | 4.9425293124000005 | 2.892599743448789 | 7.889651174410864 | 1.0764501818181818 | 7.287826909090908 | 2.6127504545454547 | 2.5123547190082642 | 6.8102477603305775 | 1.0669505454545456 | 1.1587449939363965 | 53.11242105686954 | 6.82646493772748 | 8.933354789565023 | 51.82269572048727 | 1.797774661410906 | 2.988871825549738 | 7.198798213624776 | 1.3408111952884738 |
| user_001 | Jumping | 1.446047231038e12 | 0.230521 | 0.728685 | -0.690364 | 2.714373090909091 | -5.855441727272727 | 0.19796945454545456 | 5.3139931441e-2 | 0.530981829225 | 0.476602452496 | 1.0299146630483518 | 7.593488780304369 | 2.483852090909091 | 6.584126727272727 | 0.8883334545454545 | 2.2102257685950413 | 6.819115685950414 | 1.1448584710743803 | 6.169521209513464 | 43.35072476078707 | 0.7891363264646611 | 6.836162861352534 | 51.7026766598987 | 1.7857949604733845 | 2.6146056798975508 | 7.190457333153344 | 1.3363363949520288 |
| user_001 | Jumping | 1.446047231555e12 | -0.919089 | -0.382604 | 0.114362 | 2.867653454545455 | -6.116717181818182 | 0.5776896363636365 | 0.8447245899210001 | 0.146385820816 | 1.3078667044000002e-2 | 1.0020923499263927 | 8.397198505817867 | 3.786742454545455 | 5.7341131818181825 | 0.46332763636363644 | 3.6160431239669424 | 7.249506231404959 | 1.7985207438016528 | 14.339418417056937 | 32.88005398190104 | 0.2146724986183141 | 15.941718766454512 | 64.06415040944202 | 4.433685351168226 | 3.99270819951252 | 8.004008396387526 | 2.1056318175712074 |
| user_001 | Jumping | 1.446047231749e12 | 2.29982 | -1.49389 | -1.26517 | 3.226471181818182 | -6.371024272727272 | 0.598591 | 5.2891720324 | 2.2317073320999996 | 1.6006551288999997 | 3.020187824192396 | 8.506630980416517 | 0.926651181818182 | 4.877134272727272 | 1.8637609999999998 | 3.3744030247933883 | 7.032252669421488 | 1.7465819752066116 | 0.8586824127650333 | 23.786438714210973 | 3.4736050651209993 | 15.020525370126592 | 61.25263121234784 | 4.396616733337844 | 3.875632254242731 | 7.826406021434605 | 2.096811086707108 |
| user_001 | Jumping | 1.446047232138e12 | -1.68549 | 3.67935 | -0.881966 | 2.258014090909091 | -5.886794272727272 | -0.927253090909091 | 2.8408765400999996 | 13.5376164225 | 0.7778640251560001 | 4.142023296380164 | 8.319049063739739 | 3.943504090909091 | 9.566144272727271 | 4.5287090909091e-2 | 2.489552314049587 | 7.391703132231405 | 1.3665452975206611 | 15.551224515016735 | 91.51111624663278 | 2.0509206030082726e-3 | 8.76062525277227 | 62.356581009384314 | 2.8936035211928766 | 2.959835342172309 | 7.896618327447789 | 1.701059528997406 |
| user_001 | Jumping | 1.44604723272e12 | -3.7722e-2 | -1.68549 | 2.6435 | 2.163956181818182 | -6.670619181818182 | 0.14919809090909086 | 1.422949284e-3 | 2.8408765400999996 | 6.98809225 | 3.135345553425332 | 8.126728546908634 | 2.201678181818182 | 4.985129181818182 | 2.4943019090909093 | 1.6430228429752065 | 6.649998520661156 | 1.3282255289256197 | 4.847386816294216 | 24.85151295941522 | 6.221542013694555 | 3.9847162270974086 | 50.549925642431326 | 2.8597598656943437 | 1.996175399882838 | 7.109847089947246 | 1.6910824538426101 |
| user_001 | Jumping | 1.446047233044e12 | 2.37646 | -3.29495 | -1.45677 | 2.1987926363636365 | -6.611396545454545 | -0.2723257272727273 | 5.647562131599999 | 10.856695502500001 | 2.1221788328999995 | 4.315835546797398 | 8.230510968189018 | 0.17766736363636326 | 3.3164465454545446 | 1.1844442727272726 | 1.4631399917355374 | 6.803913528925619 | 1.6708914710743803 | 3.1565692101495735e-2 | 10.998817688857383 | 1.4029082351964377 | 2.7418291416220666 | 52.41877920656386 | 3.830057240371654 | 1.6558469559781384 | 7.2400814364593895 | 1.957053203255255 |
| user_001 | Jumping | 1.446047234792e12 | 5.99e-4 | -1.11069 | -0.230521 | 3.3309803636363635 | -6.2699974545454555 | -0.9551216363636363 | 3.5880100000000004e-7 | 1.2336322760999998 | 5.3139931441e-2 | 1.1343599809328606 | 8.289652387457776 | 3.3303813636363637 | 5.159307454545456 | 0.7246006363636364 | 2.579495214876033 | 6.605979950413223 | 1.3551450247933883 | 11.091440027256406 | 26.618453410528307 | 0.5250460822185867 | 11.11725107929375 | 53.90737983996103 | 2.5507655785709034 | 3.3342542013610403 | 7.342164520082687 | 1.5971116362267552 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 | 2.7248254545454547 | -5.9285986363636365 | 0.25719245454545453 | 1.3354113599999998e-2 | 1.3078667044000002e-2 | 1.2362993721000002 | 1.1237135545787458 | 7.6763875816918326 | 2.6092654545454548 | 5.814236636363637 | 1.3690824545454545 | 2.8306369256198347 | 6.579058876033057 | 1.2154832396694213 | 6.808266212284298 | 33.805347663633135 | 1.8743867673442065 | 9.500612024374687 | 54.4232190659533 | 2.0968215239484818 | 3.082306283349318 | 7.37720943622677 | 1.4480405809052734 |
| user_001 | Jumping | 1.44604723751e12 | 1.41845 | -0.459245 | -1.38013 | 2.7457267272727273 | -5.9146635454545455 | 4.468827272727274e-2 | 2.0120004025 | 0.210905970025 | 1.9047588169000003 | 2.0316656194918004 | 7.9543184777753995 | 1.3272767272727273 | 5.455418545454545 | 1.424818272727273 | 2.351473256198347 | 6.676601545454546 | 0.9963275289256198 | 1.7616635107598018 | 29.76159150608939 | 2.0301071102975294 | 9.396080794295077 | 54.67752169176086 | 1.3210375907394523 | 3.065302724739447 | 7.394425041324096 | 1.1493639940155826 |
| user_001 | Jumping | 1.446047237964e12 | 2.6447 | -3.7722e-2 | 1.18733 | 3.101061272727273 | -5.656873181818182 | 0.2188711818181818 | 6.994438089999999 | 1.422949284e-3 | 1.4097525289 | 2.89924362001264 | 7.663649011187349 | 0.456361272727273 | 5.619151181818182 | 0.9684588181818182 | 2.099383173553719 | 6.700038338842975 | 1.0824689586776859 | 0.20826561124525644 | 31.574860004128674 | 0.9379124825141241 | 9.368198813922534 | 55.118015671780014 | 1.3014943944823985 | 3.060751347940981 | 7.424150838431289 | 1.1408305722071084 |
| user_001 | Jumping | 1.446047239453e12 | 1.26517 | -0.305964 | -5.99e-4 | 3.1811840909090914 | -5.817121727272728 | 4.468927272727272e-2 | 1.6006551288999997 | 9.361396929600001e-2 | 3.5880100000000004e-7 | 1.3016410630419586 | 7.57792755101516 | 1.9160140909090915 | 5.511157727272727 | 4.5288272727272726e-2 | 2.5278747272727267 | 6.920141305785125 | 1.1382085454545456 | 3.6711099965621923 | 30.372859494877893 | 2.0510276466198345e-3 | 9.38379122850827 | 58.07419735812502 | 1.5827917125439923 | 3.063297443688463 | 7.6206428441519956 | 1.2580905025251532 |
| user_001 | Jumping | 1.446047239777e12 | 1.80165 | -6.01569 | -0.422122 | 3.264792363636364 | -6.590495636363636 | 5.165563636363639e-2 | 3.2459427224999997 | 36.188526176100005 | 0.178186982884 | 6.293858584484084 | 8.267906869265873 | 1.463142363636364 | 0.574805636363636 | 0.4737776363636364 | 2.098750636363637 | 6.206623462809918 | 0.98239347107438 | 2.1407855762674064 | 0.33040151959540454 | 0.22446524871831408 | 6.578081373559175 | 50.15157456756155 | 1.2001631730020994 | 2.564777061180791 | 7.0817776417762195 | 1.0955195904236945 |
| user_001 | Jumping | 1.4460472401e12 | 0.996927 | 3.8919e-2 | -0.575403 | 2.8502368181818176 | -5.782284454545455 | -0.1294945454545454 | 0.993863443329 | 1.514688561e-3 | 0.331088612409 | 1.1517233801130375 | 7.488380834527844 | 1.8533098181818177 | 5.821203454545455 | 0.4459084545454546 | 2.339439314049587 | 6.414693702479339 | 1.1030548595041323 | 3.434757282169122 | 33.88640965921194 | 0.19883434983511572 | 7.295192824921782 | 51.8848540825245 | 1.7815078835544063 | 2.700961463057513 | 7.20311419335585 | 1.3347313900386124 |
| user_001 | Jumping | 1.446047240878e12 | 4.40743 | -11.61046 | 0.459245 | 2.8363007272727274 | -7.05730681818182 | -9.814018181818178e-2 | 19.425439204899998 | 134.8027814116 | 0.210905970025 | 12.427353965608487 | 9.226881520769744 | 1.5711292727272723 | 4.55315318181818 | 0.5573851818181818 | 2.7457610330578515 | 7.554485462809916 | 1.3038408595041322 | 2.4684471916205277 | 20.731203897101018 | 0.3106782409104875 | 9.850639666806897 | 63.88737605553916 | 2.4539795641499778 | 3.138572871036595 | 7.992957904026466 | 1.5665182935893145 |
| user_001 | Jumping | 1.446047241266e12 | 2.22318 | -1.80046 | 1.80046 | 2.5785103636363633 | -6.357090454545454 | 1.009665 | 4.9425293124000005 | 3.2416562115999996 | 3.2416562115999996 | 3.3802132677687657 | 8.380790617311566 | 0.35533036363636317 | 4.556630454545454 | 0.7907949999999999 | 2.8281013636363634 | 7.1047753801652895 | 2.178558561983471 | 0.12625966732195007 | 20.76288109929111 | 0.6253567320249999 | 10.393818910917538 | 58.5980634373097 | 6.668674357389767 | 3.2239446197038713 | 7.654937193557482 | 2.582377655841563 |
| user_001 | Jumping | 1.446047241913e12 | 1.07357 | -1.03405 | -0.613724 | 2.7875318181818183 | -5.768350909090909 | 0.8947032727272725 | 1.1525525448999998 | 1.0692594024999997 | 0.37665714817600005 | 1.6119767664504348 | 7.966962402686918 | 1.7139618181818184 | 4.734300909090909 | 1.5084272727272725 | 3.4725796446280985 | 6.402026396694215 | 2.2985853223140498 | 2.9376651141851244 | 22.413605097819012 | 2.2753528371074374 | 16.3891356554726 | 48.56199855497939 | 9.77557814309428 | 4.048349744707416 | 6.968643953810482 | 3.126592097331259 |
| user_001 | Jumping | 1.446047242172e12 | 6.93658 | -10.84405 | -0.422122 | 3.5504547272727276 | -6.531273454545455 | 1.068886727272727 | 48.116142096400004 | 117.59342040249999 | 0.178186982884 | 12.879741825121496 | 8.868355706458432 | 3.3861252727272726 | 4.312776545454544 | 1.4910087272727268 | 3.5162844710743797 | 6.2994165371900825 | 2.3676246446280995 | 11.465844362602347 | 18.600041531022832 | 2.223107024803437 | 16.457890970833663 | 47.36959226026294 | 9.601871044209373 | 4.056832627904886 | 6.882557101852693 | 3.0986886007163372 |
| user_001 | Jumping | 1.446047242884e12 | 10.50036 | -19.58108 | 1.8771 | 5.0588799999999985 | -7.639077636363638 | -0.2862584545454545 | 110.25756012960001 | 383.4186939664 | 3.52350441 | 22.297976556315596 | 10.558958547804354 | 5.441480000000002 | 11.942002363636362 | 2.1633584545454543 | 4.1826142231404955 | 7.356550206611572 | 1.5752493140495865 | 29.60970459040002 | 142.61142045309646 | 4.680119802853296 | 28.286595726482048 | 63.57031171260442 | 3.541155966070285 | 5.318514428530024 | 7.973099253903993 | 1.881795941665909 |
| user_001 | Jumping | 1.446047243143e12 | 0.307161 | 1.53341 | -2.49142 | 3.6340609090909086 | -5.660356454545454 | -0.3315469090909091 | 9.434787992100001e-2 | 2.3513462280999997 | 6.207173616400001 | 2.941575721347489 | 8.185633262013813 | 3.3268999090909084 | 7.193766454545454 | 2.159873090909091 | 3.1970538347107436 | 6.646516429752068 | 1.4789741074380165 | 11.068263005109095 | 51.75027580254347 | 4.665051768833191 | 14.673029832308737 | 51.88884227807641 | 2.987737094353247 | 3.8305391046572983 | 7.2033910263206185 | 1.7285071866652006 |
| user_001 | Jumping | 1.446047243531e12 | 11.57333 | -19.58108 | 0.344284 | 4.5014933636363645 | -7.2245218181818185 | 4.817318181818176e-2 | 133.9419672889 | 383.4186939664 | 0.11853147265599999 | 22.748168997261207 | 9.614007130330195 | 7.071836636363636 | 12.356558181818182 | 0.2961108181818182 | 3.602741462809917 | 7.240955603305786 | 1.341211165289256 | 50.01087341141494 | 152.68453010065784 | 8.768161664430581e-2 | 18.025288293019972 | 62.05824709530112 | 2.6538719664479 | 4.245619895023573 | 7.877705699967543 | 1.6290708905532318 |
| user_001 | Jumping | 1.446047244373e12 | -0.535886 | -1.18733 | 1.64717 | 4.111323636363637 | -5.952984272727273 | -0.17478236363636365 | 0.287173804996 | 1.4097525289 | 2.7131690089 | 2.10002270054302 | 8.42056546855258 | 4.647209636363637 | 4.765654272727272 | 1.8219523636363637 | 3.491582256198347 | 6.728223553719008 | 0.8411461652892562 | 21.596557404311042 | 22.711460647163708 | 3.3195104153601327 | 18.261239277957863 | 53.18916722596664 | 1.0814781705921834 | 4.273317128175472 | 7.293090375551824 | 1.0399414265198705 |
| user_001 | Jumping | 1.446047244502e12 | -1.72382 | 0.690364 | 0.191003 | 3.6027080000000002 | -5.8658920909090915 | 1.6818909090909072e-2 | 2.9715553923999996 | 0.476602452496 | 3.6482146009e-2 | 1.8667190444480388 | 8.710287378942498 | 5.326528 | 6.556256090909091 | 0.17418409090909093 | 4.041685214876034 | 6.887205876033058 | 0.6983162396694215 | 28.371900534783997 | 42.984493929582555 | 3.034009752582645e-2 | 24.406443491662195 | 55.8220753644146 | 0.7729212899290271 | 4.9402877944166566 | 7.471417225962863 | 0.8791594223626492 |
| user_001 | Standing | 1.446047263599e12 | 1.22685 | -9.27292 | 0.191003 | 1.1188554545454545 | -9.346076363636364 | 0.40002272727272725 | 1.5051609225 | 85.98704532639998 | 3.6482146009e-2 | 9.355676800473015 | 9.427912904892985 | 0.10799454545454545 | 7.315636363636457e-2 | 0.20901972727272725 | 0.213428500603437 | 8.054413590450014e-2 | 0.1583984693755739 | 1.1662821847933885e-2 | 5.351853540496005e-3 | 4.368924638916528e-2 | 7.091546002166214e-2 | 9.146781793338572e-3 | 3.574611894166528e-2 | 0.26629956819653716 | 9.56388090334597e-2 | 0.18906644054846244 |
| user_001 | Standing | 1.446047265606e12 | 1.22685 | -9.2346 | -0.1922 | 0.9516395454545455 | -9.36001090909091 | -0.2200696363636364 | 1.5051609225 | 85.27783716 | 3.694084e-2 | 9.317721766746418 | 9.41792726022171 | 0.27521045454545445 | 0.12541090909090968 | 2.7869636363636402e-2 | 0.2001527107438017 | 0.10862611570247971 | 0.20300272727272725 | 7.574079429111565e-2 | 1.5727896119008412e-2 | 7.767166310413244e-4 | 6.74780467919369e-2 | 1.5294319858151866e-2 | 5.381838598250938e-2 | 0.25976536873097017 | 0.12367020602453878 | 0.23198790050886142 |
| user_001 | Standing | 1.446047265736e12 | 1.03525 | -9.46452 | -0.613724 | 0.9934438181818183 | -9.373945454545453 | -0.27580827272727276 | 1.0717425625 | 89.5771388304 | 0.37665714817600005 | 9.540730503534622 | 9.43637041659947 | 4.180618181818174e-2 | 9.057454545454746e-2 | 0.3379157272727273 | 0.18526814049586776 | 0.12287735537190142 | 0.2039529008264463 | 1.7477568382148697e-3 | 8.203748284297884e-3 | 0.11418703873825621 | 5.9142881348738546e-2 | 1.964663117896333e-2 | 5.5500906570311793e-2 | 0.24319309478013257 | 0.1401664409870042 | 0.23558630386826776 |
| user_001 | Standing | 1.446047265929e12 | 0.958607 | -9.34956 | -0.230521 | 0.9795095454545454 | -9.384396363636363 | -0.32806318181818184 | 0.918927380449 | 87.41427219360001 | 5.3139931441e-2 | 9.401400933131722 | 9.44541709001559 | 2.0902545454545396e-2 | 3.483636363636222e-2 | 9.754218181818183e-2 | 0.13142985950413222 | 9.595834710743874e-2 | 0.1992023388429752 | 4.369164064793364e-4 | 1.2135722314048601e-3 | 9.514477233851243e-3 | 2.979612839872803e-2 | 1.4155768455597456e-2 | 5.6693495851969186e-2 | 0.17261555086007757 | 0.11897801669046873 | 0.23810396017699745 |
| user_001 | Standing | 1.446047269426e12 | 0.690364 | -9.34956 | 3.7722e-2 | 0.6555278181818183 | -9.401814545454544 | 0.13178036363636367 | 0.476602452496 | 87.41427219360001 | 1.422949284e-3 | 9.375089204662535 | 9.427105421840285 | 3.483618181818171e-2 | 5.225454545454333e-2 | 9.405836363636366e-2 | 0.11147730578512394 | 4.3703801652892824e-2 | 0.10831033057851239 | 1.213559563669414e-3 | 2.7305375206609353e-3 | 8.846975769950418e-3 | 1.8563579805789626e-2 | 3.104538417430562e-3 | 2.244263762957626e-2 | 0.1362482286335849 | 5.571838491405294e-2 | 0.1498086700747866 |
| user_001 | Standing | 1.446047269555e12 | 0.652044 | -9.4262 | 0.267643 | 0.6311421818181817 | -9.401814545454544 | 0.14919863636363637 | 0.42516137793599995 | 88.85324643999999 | 7.163277544900001e-2 | 9.452515040632573 | 9.425761237002126 | 2.0901818181818244e-2 | 2.4385454545456042e-2 | 0.11844436363636365 | 0.10736015702479335 | 4.5287272727273245e-2 | 8.297457851239672e-2 | 4.368860033057877e-4 | 5.946503933885027e-4 | 1.4029067277223145e-2 | 1.902580785260781e-2 | 3.1630105340346294e-3 | 1.5928957561460556e-2 | 0.13793407067366573 | 5.6240648414066396e-2 | 0.1262099740965846 |
| user_001 | Standing | 1.446047271885e12 | 0.652044 | -9.57948 | -5.99e-4 | 0.7495870909090908 | -9.520258181818182 | -0.3385142727272727 | 0.42516137793599995 | 91.7664370704 | 3.5880100000000004e-7 | 9.60164563015825 | 9.56080406607656 | 9.754309090909086e-2 | 5.9221818181818264e-2 | 0.3379152727272727 | 0.2087035702479339 | 0.22263603305785085 | 0.15169771074380167 | 9.514654584099164e-3 | 3.5072237487603405e-3 | 0.11418673154234707 | 6.712845657103984e-2 | 6.522730094305014e-2 | 2.9760565016948907e-2 | 0.259091598804438 | 0.2553963604733829 | 0.1725125068421096 |
| user_001 | Standing | 1.446047274022e12 | 0.460442 | -9.69444 | -0.383802 | 0.540567 | -9.440133636363637 | -0.1643308181818182 | 0.21200683536400003 | 93.9821669136 | 0.147303975204 | 9.712954119327858 | 9.464250160317123 | 8.0125e-2 | 0.2543063636363634 | 0.2194711818181818 | 0.18875167768595047 | 0.20870247933884284 | 0.2182040743801653 | 6.420015625e-3 | 6.467172658595029e-2 | 4.816759964866941e-2 | 5.836297226791512e-2 | 5.4720719320510834e-2 | 6.432368563568823e-2 | 0.2415842964017221 | 0.233924601785513 | 0.2536211458764593 |
| user_001 | Standing | 1.44604727441e12 | -0.689167 | -8.77475 | -0.537083 | 0.30019381818181823 | -9.454068181818181 | -9.465763636363635e-2 | 0.47495115388899994 | 76.99623756249999 | 0.28845814888899995 | 8.818143050851353 | 9.480012502114672 | 0.9893608181818182 | 0.6793181818181822 | 0.44242536363636364 | 0.29896195041322315 | 0.25240603305785103 | 0.3337983388429752 | 0.9788348285533968 | 0.4614731921487608 | 0.1957402023887686 | 0.16011038759520513 | 9.532066431217111e-2 | 0.20969082450808935 | 0.4001379607025621 | 0.30874044813106544 | 0.45792010712360004 |
| user_001 | Standing | 1.446047274668e12 | -3.7722e-2 | -9.4262 | 7.6042e-2 | 0.1991675454545455 | -9.422714545454546 | -3.891909090909091e-2 | 1.422949284e-3 | 88.85324643999999 | 5.782385764e-3 | 9.426582189481403 | 9.444384183993192 | 0.23688954545454552 | 3.485454545453237e-3 | 0.1149610909090909 | 0.29516157024793394 | 0.2492403305785123 | 0.31954693388429756 | 5.6116656745661186e-2 | 1.2148393388420632e-5 | 1.3216052423008263e-2 | 0.15389679360633812 | 9.846773534515398e-2 | 0.19812968471057016 | 0.3922968182465136 | 0.31379569045025774 | 0.4451176077292047 |
| user_001 | Standing | 1.446047274733e12 | 0.11556 | -9.65612 | 0.152682 | 0.1678146363636364 | -9.419230909090908 | 9.852181818181826e-3 | 1.3354113599999998e-2 | 93.24065345439999 | 2.3311793124000002e-2 | 9.65801839722435 | 9.439390027438327 | 5.225463636363639e-2 | 0.2368890909090915 | 0.1428298181818182 | 0.29262790082644635 | 0.24765694214876033 | 0.31257953719008263 | 2.730547021495871e-3 | 5.6116441391735813e-2 | 2.0400356961851242e-2 | 0.15356138736965594 | 9.768998214567995e-2 | 0.19560538992085943 | 0.39186909468552883 | 0.3125539667732277 | 0.44227298122410713 |
| user_001 | Standing | 1.446047275575e12 | -0.152682 | -9.54116 | 0.229323 | -0.30596372727272725 | -9.506323636363637 | 0.17706818181818182 | 2.3311793124000002e-2 | 91.0337341456 | 5.2589038329e-2 | 9.545136718615035 | 9.515732424195958 | 0.15328172727272724 | 3.483636363636222e-2 | 5.225481818181818e-2 | 0.25589105785123967 | 0.1260450413223138 | 0.11084372727272726 | 2.3495287915710733e-2 | 1.2135722314048601e-3 | 2.730566023214876e-3 | 9.74116867501788e-2 | 2.1687781249962387e-2 | 1.560012405370323e-2 | 0.3121084535064355 | 0.14726771964677932 | 0.12490045657924245 |
| user_001 | Standing | 1.446047275834e12 | 3.8919e-2 | -9.54116 | -7.7239e-2 | -0.24674145454545454 | -9.506323636363637 | 0.14223154545454544 | 1.514688561e-3 | 91.0337341456 | 5.965863121e-3 | 9.541552006737794 | 9.513969773424815 | 0.2856604545454545 | 3.483636363636222e-2 | 0.21947054545454545 | 0.1719664297520661 | 9.374181818181816e-2 | 0.13016225619834712 | 8.16018952911157e-2 | 1.2135722314048601e-3 | 4.81673203221157e-2 | 4.6993579989857245e-2 | 1.1213484001051882e-2 | 2.176627511864989e-2 | 0.21678002673183996 | 0.10589373919666772 | 0.1475339795391214 |
| user_001 | Standing | 1.446047276546e12 | 0.652044 | -9.54116 | 0.152682 | 0.20265118181818179 | -9.426200000000001 | 0.16661700000000002 | 0.42516137793599995 | 91.0337341456 | 2.3311793124000002e-2 | 9.564633151180447 | 9.434537545263662 | 0.4493928181818182 | 0.11495999999999817 | 1.3935000000000003e-2 | 0.22042100826446284 | 9.81752066115703e-2 | 9.627585123966943e-2 | 0.20195390503339672 | 1.321580159999958e-2 | 1.9418422500000007e-4 | 0.10771836667435462 | 1.439737965439517e-2 | 1.6308444580647636e-2 | 0.3282047633328234 | 0.1199890813965803 | 0.12770452059597434 |
| user_001 | Standing | 1.44604727674e12 | 0.498763 | -9.4262 | 0.114362 | 0.2862592727272727 | -9.422716363636363 | 0.15616609090909092 | 0.24876453016900002 | 88.85324643999999 | 1.3078667044000002e-2 | 9.440078899946387 | 9.433625006433285 | 0.21250372727272732 | 3.4836363636365775e-3 | 4.1804090909090916e-2 | 0.19666888429752066 | 7.062280991735535e-2 | 0.1092603305785124 | 4.515783410480167e-2 | 1.2135722314051077e-5 | 1.7475820167355378e-3 | 7.172443230365816e-2 | 9.10730797295259e-3 | 1.7223049239172054e-2 | 0.2678141749490832 | 9.543221664067429e-2 | 0.13123661546676696 |
| user_001 | Standing | 1.446047278358e12 | 0.230521 | -9.50284 | 7.6042e-2 | 0.4743771818181818 | -9.429683636363634 | -4.937e-2 | 5.3139931441e-2 | 90.30396806560002 | 5.782385764e-3 | 9.505939742224596 | 9.44275825839253 | 0.24385618181818178 | 7.315636363636635e-2 | 0.125412 | 9.817605785123967e-2 | 6.61890909090919e-2 | 7.347361983471073e-2 | 5.946583741094213e-2 | 5.351853540496265e-3 | 1.5728169743999997e-2 | 1.5050738304140497e-2 | 5.351853540495982e-3 | 7.956755875627348e-3 | 0.1226814505299823 | 7.315636363636442e-2 | 8.920064952469431e-2 |
| user_001 | Standing | 1.446047278487e12 | 0.575403 | -9.50284 | -0.11556 | 0.45695881818181816 | -9.412265454545455 | -4.588627272727274e-2 | 0.331088612409 | 90.30396806560002 | 1.3354113599999998e-2 | 9.5209458979457 | 9.424269167781597 | 0.11844418181818184 | 9.057454545454569e-2 | 6.967372727272725e-2 | 8.329125619834712e-2 | 6.745586776859579e-2 | 6.080570247933885e-2 | 1.4029024206578517e-2 | 8.203748284297563e-3 | 4.854428272074377e-3 | 1.1854561421095417e-2 | 5.316549621036888e-3 | 5.860540686942148e-3 | 0.10887865457056042 | 7.29146735646323e-2 | 7.655416831853212e-2 |
| user_001 | Walking | 1.446047180284e12 | 2.87462 | -7.5485 | -0.652044 | 2.855461666666667 | -7.082271666666666 | -0.6264973333333334 | 8.2634401444 | 56.97985224999999 | 0.42516137793599995 | 8.103607454235181 | 7.800958151180336 | 1.915833333333339e-2 | 0.46622833333333347 | 2.5546666666666606e-2 | 0.34850305555555555 | 1.337910444444444 | 0.49145825 | 3.670417361111132e-4 | 0.2173688588027779 | 6.526321777777747e-4 | 0.2600767203810184 | 2.732268649286924 | 0.5559198368820787 | 0.5099771763334301 | 1.6529575461235912 | 0.7456003197974628 |
| user_001 | Walking | 1.446047181772e12 | 2.98958 | -10.95901 | 0.459245 | 3.8395972727272722 | -8.569218181818183 | -3.891881818181819e-2 | 8.937588576400001 | 120.09990018009998 | 0.210905970025 | 11.368746400836153 | 9.573815275666846 | 0.8500172727272721 | 2.3897918181818163 | 0.4981638181818182 | 1.1372585950413223 | 1.8580585123966946 | 1.0203968016528926 | 0.7225293639347097 | 5.711104934248751 | 0.24816718974548763 | 1.9972560146706988 | 4.208256607415629 | 1.4502039611254238 | 1.413243084069651 | 2.0514035700991724 | 1.2042441451489079 |
| user_001 | Walking | 1.446047182355e12 | 3.75599 | -9.2346 | -0.1922 | 3.393686363636363 | -9.238079999999998 | -0.14691309090909088 | 14.107460880100001 | 85.27783716 | 3.694084e-2 | 9.971070097040739 | 9.959120372594569 | 0.36230363636363716 | 3.479999999997929e-3 | 4.528690909090913e-2 | 1.0903866942148766 | 1.489106859504132 | 0.8702828760330577 | 0.13126392492231462 | 1.2110399999985587e-5 | 2.0509041350082677e-3 | 1.7039711227195353 | 3.383287766617504 | 1.6472040493657807 | 1.3053624487932596 | 1.8393715683943535 | 1.2834344741223764 |
| user_001 | Walking | 1.446047183131e12 | 2.14654 | -7.01202 | -0.1922 | 3.9336563636363646 | -7.990928181818183 | -0.33503054545454547 | 4.607633971599999 | 49.1684244804 | 3.694084e-2 | 7.335734407133344 | 9.001829187315739 | 1.7871163636363647 | 0.9789081818181833 | 0.14283054545454546 | 1.3754151239669425 | 2.065812727272728 | 0.49879744628099176 | 3.1937848971768634 | 0.9582612284305814 | 2.0400564714842976e-2 | 3.1333928490780623 | 4.903279528126523 | 0.3921236151622036 | 1.7701392174284096 | 2.214335008106615 | 0.6261977444563368 |
| user_001 | Walking | 1.446047183391e12 | 6.13185 | -8.42987 | 0.880768 | 4.139193636363636 | -9.272917272727272 | -0.15736363636363637 | 37.5995844225 | 71.06270821689999 | 0.775752269824 | 10.461264020625041 | 10.25392163802889 | 1.992656363636364 | 0.8430472727272722 | 1.0381316363636364 | 1.1625942148760335 | 1.8175228925619842 | 0.4946802396694215 | 3.9706793835404968 | 0.7107287040528917 | 1.0777172944190414 | 2.1014859787929385 | 4.047375322672654 | 0.44549834427541024 | 1.449650295344687 | 2.0118089677384017 | 0.6674566235160231 |
| user_001 | Walking | 1.446047183456e12 | 5.17384 | -10.88237 | -2.29982 | 3.9510754545454536 | -9.25549909090909 | -0.4012203636363637 | 26.768620345600002 | 118.4259768169 | 5.2891720324 | 12.267182610318475 | 10.165673647936613 | 1.2227645454545466 | 1.6268709090909095 | 1.8985996363636364 | 0.9918942148760336 | 1.746266198347108 | 0.5687874876033058 | 1.495153133620664 | 2.6467089548462823 | 3.6046805792001324 | 1.3635107729747569 | 3.7596717557055617 | 0.6664876060740993 | 1.1676946402954655 | 1.9389873015844024 | 0.8163869217926628 |
| user_001 | Walking | 1.446047183909e12 | 7.66466 | -11.99366 | 0.574206 | 4.132225454545455 | -8.624955454545455 | -0.29322699999999996 | 58.747012915599996 | 143.8478801956 | 0.329712530436 | 14.245160779774864 | 9.681523051406105 | 3.532434545454545 | 3.368704545454545 | 0.8674329999999999 | 1.3538776859504131 | 1.8552098347107444 | 0.6131255950413222 | 12.478093817920657 | 11.348170314566111 | 0.7524400094889998 | 2.618715063950863 | 4.667012944941023 | 0.6613691747166004 | 1.6182444388753088 | 2.160327045829178 | 0.8132460726721036 |
| user_001 | Walking | 1.44604718488e12 | 3.18118 | -6.85874 | 0.727487 | 1.8748092727272727 | -8.635407272727273 | -0.41863927272727275 | 10.1199061924 | 47.0423143876 | 0.529237335169 | 7.595489313742005 | 8.965360270876873 | 1.3063707272727272 | 1.7766672727272725 | 1.1461262727272727 | 1.3057410578512398 | 1.844758760330579 | 0.6365607685950413 | 1.7066044770750741 | 3.1565465979801646 | 1.3136054330357108 | 2.910001133474229 | 5.0640287330080405 | 0.5493715684342967 | 1.7058725431503459 | 2.2503396928037422 | 0.7411960391382948 |
| user_001 | Walking | 1.446047185139e12 | 5.25048 | -9.88604 | 1.22565 | 3.484263 | -8.851394545454545 | 0.13526336363636363 | 27.567540230399995 | 97.7337868816 | 1.5022179224999999 | 11.260708016572492 | 9.724801616813902 | 1.7662169999999997 | 1.0346454545454549 | 1.0903866363636363 | 1.464406355371901 | 1.93628520661157 | 0.8553982314049587 | 3.119522491088999 | 1.070491216611571 | 1.188943016760405 | 3.2545971208863858 | 5.148444138564013 | 0.9023512724995583 | 1.8040501991037794 | 2.26901832045579 | 0.949921719142982 |
| user_001 | Walking | 1.446047186498e12 | 2.10822 | -7.70178 | 1.99206 | 3.041838545454546 | -8.321874545454545 | 0.21887154545454549 | 4.444591568400001 | 59.317415168400004 | 3.9683030435999997 | 8.229842633999754 | 9.396426436216277 | 0.9336185454545456 | 0.6200945454545446 | 1.7731884545454544 | 2.387577404958678 | 1.9039815702479344 | 1.6145203388429754 | 0.8716435884166615 | 0.3845172453024782 | 3.144197295333297 | 7.658007098651109 | 5.2602666817868515 | 3.48402192641333 | 2.7673104449358603 | 2.2935271268914286 | 1.8665534887630009 |
| user_001 | Walking | 1.446047186822e12 | 6.43841 | -10.26924 | 1.76214 | 4.160093727272727 | -9.980099090909091 | 0.7170357272727274 | 41.453123328100006 | 105.4572901776 | 3.1051373796000004 | 12.248083559696186 | 11.236332039074833 | 2.278316272727273 | 0.28914090909090895 | 1.0451042727272726 | 2.1487878099173554 | 1.5632160330578513 | 1.4099342892561983 | 5.190725038573894 | 8.360246530991727e-2 | 1.0922429408728014 | 6.734969483419675 | 4.5926103210730265 | 2.969415216736809 | 2.5951819750105534 | 2.143037638743899 | 1.7231991227762418 |
| user_001 | Walking | 1.446047188051e12 | 7.7239e-2 | -12.30022 | 3.06503 | 1.6832087272727272 | -10.509615454545456 | 1.3336453636363637 | 5.965863121e-3 | 151.29541204839998 | 9.3944089009 | 12.676584193402455 | 11.163492266098267 | 1.6059697272727271 | 1.7906045454545438 | 1.7313846363636365 | 1.7152285289256197 | 1.5055769421487601 | 1.262353876033058 | 2.5791387649164377 | 3.2062646382024735 | 2.997692759036042 | 5.070047780984225 | 3.0689191772743043 | 3.7338152547223733 | 2.2516766599545823 | 1.7518330905866302 | 1.9323082711416346 |
| user_001 | Walking | 1.446047188311e12 | 0.268841 | -6.59049 | -1.68669 | 1.3487765454545457 | -9.25201272727273 | 1.1106903636363639 | 7.2275483281e-2 | 43.4345584401 | 2.8449231561 | 6.808212473144548 | 9.785277241718822 | 1.0799355454545458 | 2.6615227272727298 | 2.797380363636364 | 1.8925786776859506 | 2.2517144628099177 | 0.9893604297520661 | 1.1662607823362074 | 7.083703227789269 | 7.825336898858317 | 4.470154434567544 | 6.05348710545417 | 1.7177182240601185 | 2.114273973393123 | 2.4603835281220223 | 1.310617497235604 |
| user_001 | Walking | 1.446047188958e12 | 4.98224 | -8.92803 | 2.60518 | 2.4112949090909095 | -9.377426363636364 | 1.3127427272727272 | 24.8227154176 | 79.7097196809 | 6.7869628323999995 | 10.550800819411766 | 10.216843446454602 | 2.5709450909090905 | 0.4493963636363638 | 1.2924372727272726 | 1.9391346694214875 | 1.1451766942148764 | 1.3681292561983474 | 6.609758660469551 | 0.20195709164958695 | 1.6703941039347103 | 6.863680146541103 | 2.0560275006287 | 2.8299361702762655 | 2.619862619783927 | 1.433885455895519 | 1.6822414126029193 |
| user_001 | Walking | 1.44604718967e12 | 2.4531 | -12.41518 | 2.22198 | 1.7319801818181815 | -10.004485454545454 | 1.525245909090909 | 6.01769961 | 154.1366944324 | 4.937195120399999 | 12.848797187394624 | 10.75444663207888 | 0.7211198181818186 | 2.410694545454545 | 0.6967340909090909 | 2.424313338842975 | 2.043328099173554 | 1.2645705123966944 | 0.5200137921745791 | 5.8114481914842955 | 0.48543839343491735 | 8.661377711842006 | 4.848658038340046 | 2.34287558758228 | 2.9430218673740782 | 2.2019668567760156 | 1.530645480698349 |
| user_001 | Walking | 1.446047189928e12 | 0.920286 | -7.1653 | 3.02671 | 1.3069736363636366 | -8.980288181818183 | 1.1385597272727273 | 0.8469263217960001 | 51.34152409 | 9.1609734241 | 7.8325873015176795 | 9.602183139299719 | 0.38668763636363657 | 1.8149881818181832 | 1.8881502727272728 | 2.1893249090909097 | 1.8172056198347109 | 1.335193603305785 | 0.14952732811649602 | 3.2941821001396745 | 3.5651114524000747 | 8.159652943115212 | 4.097719352706086 | 2.4497499611766536 | 2.856510623665736 | 2.024282429085943 | 1.565167710239594 |
| user_001 | Walking | 1.446047190446e12 | 0.613724 | -10.69077 | 1.34061 | 2.1326020000000003 | -9.642186363636364 | 2.2115288181818182 | 0.37665714817600005 | 114.2925631929 | 1.7972351721000002 | 10.791962542243 | 10.304806342778686 | 1.5188780000000004 | 1.0485836363636363 | 0.8709188181818182 | 1.2173821157024796 | 1.269636363636364 | 1.0451003140495867 | 2.3069903788840014 | 1.0995276424495866 | 0.7584995878632148 | 2.15967125426067 | 2.1969245967035316 | 1.3724574162477086 | 1.46958199984236 | 1.4822026166160724 | 1.1715192769424276 |
| user_001 | Walking | 1.44604719077e12 | 9.58068 | -11.11229 | 2.72014 | 2.484452545454545 | -9.290335454545454 | 1.6367235454545452 | 91.78942926239999 | 123.4829890441 | 7.399161619599999 | 14.922184154007079 | 10.185253490166119 | 7.0962274545454544 | 1.8219545454545454 | 1.0834164545454545 | 2.4334978677685952 | 1.7202968595041321 | 1.335827578512397 | 50.35644408664466 | 3.319518365702479 | 1.173791213979843 | 8.751717569815908 | 3.9151569891035307 | 1.9819154423742382 | 2.9583301995916393 | 1.978675564387333 | 1.4078051862293441 |
| user_001 | Walking | 1.4460471909e12 | 2.33814 | -10.34589 | 0.267643 | 2.334655272727273 | -9.57948 | 1.5775010909090907 | 5.466898659600001 | 107.03743989210001 | 7.163277544900001e-2 | 10.61018243609171 | 10.412488909538352 | 3.484727272727195e-3 | 0.7664100000000005 | 1.3098580909090907 | 2.286234066115703 | 1.961620826446281 | 1.4811914132231407 | 1.2143324165288713e-5 | 0.5873842881000008 | 1.7157282183200075 | 8.53354395055349 | 4.807577774175207 | 2.450414720949974 | 2.92122302307672 | 2.192618930451711 | 1.5653800563920488 |
| user_001 | Walking | 1.446047191029e12 | 7.7239e-2 | -10.65245 | 1.76214 | 1.3940660909090907 | -9.54116 | 1.208232909090909 | 5.965863121e-3 | 113.4746910025 | 3.1051373796000004 | 10.797490182686946 | 10.140294446078387 | 1.3168270909090907 | 1.1112900000000003 | 0.5539070909090911 | 2.349573669421488 | 1.9293182644628102 | 1.2848401239669422 | 1.7340335873520984 | 1.2349654641000007 | 0.3068130653593721 | 8.852159317555563 | 4.5452264330368894 | 2.0859129017722275 | 2.975257857321876 | 2.131953665780964 | 1.4442689852559416 |
| user_001 | Walking | 1.44604719472e12 | 2.98958 | -6.93538 | 1.45557 | 2.6307660000000004 | -8.617989090909091 | 0.16313263636363637 | 8.937588576400001 | 48.0994957444 | 2.1186840249000003 | 7.69127871980336 | 9.496991123941198 | 0.35881399999999974 | 1.6826090909090912 | 1.2924373636363637 | 2.500954809917355 | 1.4875247933884297 | 1.4146848347107435 | 0.12874748659599983 | 2.8311733528099183 | 1.6703943389233142 | 8.934629473100316 | 3.4043500566503395 | 3.024069247093168 | 2.9890850561836335 | 1.845088089130256 | 1.7389851198596173 |
| user_001 | Walking | 1.446047196662e12 | 2.18486 | -7.7401 | 0.229323 | 2.780563363636364 | -8.914100000000001 | 0.22583936363636362 | 4.7736132196000005 | 59.90914801 | 5.2589038329e-2 | 8.045828128162384 | 9.628036125244144 | 0.595703363636364 | 1.1740000000000013 | 3.483636363636383e-3 | 1.2908563966942153 | 1.6157874380165291 | 0.9842932231404959 | 0.3548624974476781 | 1.378276000000003 | 1.2135722314049723e-5 | 3.2097369116515084 | 3.6398387260136755 | 1.8849218529539593 | 1.7915738644140544 | 1.9078361370971237 | 1.3729245620040307 |
| user_002 | Jogging | 1.446034899502e12 | -5.17264 | -2.29862 | -0.652044 | -6.590493333333334 | -7.893386500000001 | 8.242900000000003e-2 | 26.756204569600005 | 5.2836539044 | 0.42516137793599995 | 5.697808337592272 | 12.152165231871896 | 1.4178533333333334 | 5.5947665 | 0.7344729999999999 | 4.609720444444445 | 4.239504491666666 | 0.5881105999999999 | 2.0103080748444446 | 31.301412189522253 | 0.5394505877289999 | 33.81042934724859 | 28.60576982987043 | 0.6282389243875768 | 5.814673623450296 | 5.348436204150745 | 0.7926152436003088 |
| user_002 | Jogging | 1.446034899567e12 | -4.3296 | 1.38013 | -2.49142 | -6.267508571428571 | -6.568598428571429 | -0.2852637142857143 | 18.74543616 | 1.9047588169000003 | 6.207173616400001 | 5.182409535467069 | 11.156485846671206 | 1.9379085714285713 | 7.948728428571429 | 2.206156285714286 | 4.228033034013606 | 4.769393625510204 | 0.8192599836734694 | 3.755489631216326 | 63.18228363117962 | 4.867125556996655 | 29.516866530672555 | 33.54527180148603 | 1.2337941576174452 | 5.4329427137300605 | 5.791828018983819 | 1.110762871911663 |
| user_002 | Jogging | 1.446034899696e12 | -15.48081 | -11.4955 | -5.40376 | -6.743775555555555 | -7.092917666666668 | -0.554114 | 239.6554782561 | 132.14652025 | 29.2006221376 | 20.025049828744496 | 11.672709526212666 | 8.737034444444445 | 4.402582333333332 | 4.849646 | 4.738255770282188 | 4.218929176322751 | 1.4384393484126983 | 76.33577088340866 | 19.382731201778768 | 23.519066325316 | 33.50431890521451 | 28.248085207477747 | 4.192472464836242 | 5.788291535955537 | 5.3148927747865 | 2.0475527990350435 |
| user_002 | Jogging | 1.446034900085e12 | -11.22725 | -13.18159 | 1.60885 | -7.1234945454545455 | -5.629004272727273 | -1.2582030909090909 | 126.05114256249999 | 173.7543149281 | 2.5883983225 | 17.38947543237288 | 11.16904854705825 | 4.103755454545454 | 7.552585727272727 | 2.8670530909090908 | 5.567430203233635 | 3.9994831554210943 | 2.3141057098583233 | 16.840808830711566 | 57.04155116780371 | 8.21999342609137 | 48.62458743568818 | 23.34982360647302 | 8.807010229135319 | 6.973133258133547 | 4.8321655193580675 | 2.9676607334962193 |
| user_002 | Jogging | 1.446034900667e12 | 0.230521 | -0.229323 | 1.91542 | -9.39136081818182 | -6.583527272727272 | -1.310457 | 5.3139931441e-2 | 5.2589038329e-2 | 3.6688337763999996 | 1.9428233955174616 | 12.346907069341832 | 9.62188181818182 | 6.354204272727272 | 3.2258769999999997 | 5.931096528925619 | 4.556951537190082 | 2.00754094214876 | 92.58060972305788 | 40.37591193954552 | 10.406282419128997 | 44.472090029696176 | 27.521761508496457 | 6.616798714486936 | 6.668739763230844 | 5.246118708959649 | 2.5723138833522894 |
| user_002 | Jogging | 1.446034901185e12 | -5.36425 | -8.54483 | 0.191003 | -7.611209 | -5.844989454545455 | -1.171110909090909 | 28.7751780625 | 73.01411972889998 | 3.6482146009e-2 | 10.090876073830705 | 10.960188536893574 | 2.2469589999999995 | 2.6998405454545447 | 1.3621139090909091 | 6.355472297520662 | 4.523063793388429 | 1.7697027685950415 | 5.048824747680998 | 7.289138970880293 | 1.8553543013389175 | 54.723412450197195 | 24.298524186578423 | 5.412217046523582 | 7.397527455183738 | 4.9293533233658975 | 2.3264172124800795 |
| user_002 | Jogging | 1.446034901249e12 | -16.66874 | -11.99366 | -6.9749 | -7.443993545454545 | -6.134134 | -1.1815618181818182 | 277.8468931876 | 143.8478801956 | 48.64923001 | 21.687415784117757 | 10.966622719039162 | 9.224746454545453 | 5.859526 | 5.793338181818182 | 6.216442719008264 | 4.845461561983471 | 1.7750865454545457 | 85.09594715064891 | 34.334044944675995 | 33.562767288912404 | 51.94576576656416 | 26.933376868281997 | 5.474278291022689 | 7.207341102415242 | 5.189737649273034 | 2.339717566507267 |
| user_002 | Jogging | 1.44603490143e12 | 4.33079 | 0.15388 | -2.68302 | 0.7809381818181816 | -1.270939272727273 | -0.24793854545454552 | 18.7557420241 | 2.3679054399999996e-2 | 7.1985963204 | 5.096863486390429 | 4.048117719778665 | 3.5498518181818186 | 1.424819272727273 | 2.4350814545454544 | 5.414564024793389 | 1.6344727685950415 | 2.4129134132231407 | 12.601447931048764 | 2.0301099599350754 | 5.9296216902712064 | 31.92965352210678 | 2.823666219108468 | 6.823117529926936 | 5.6506330195922985 | 1.6803768086677666 | 2.6121097851979607 |
| user_002 | Jogging | 1.446034901543e12 | -6.7821 | -8.35323 | -0.15388 | -1.9293514545454549 | -4.0265189090909095 | -1.0596333636363637 | 45.996880409999996 | 69.7764514329 | 2.3679054399999996e-2 | 10.760901955565807 | 6.529144512249854 | 4.852748545454545 | 4.3267110909090905 | 0.9057533636363637 | 3.989429066115703 | 2.2393638842975205 | 1.7814196280991736 | 23.549168445411198 | 18.720428864195732 | 0.820389155738587 | 21.84543006964122 | 6.035619134752426 | 4.882249051245194 | 4.673909505931968 | 2.456749709423495 | 2.2095811936304113 |
| user_002 | Jogging | 1.446034901568e12 | -13.90968 | -14.25456 | 2.37526 | -5.576748727272728 | -7.1200127272727265 | 0.685683 | 193.4791977024 | 203.1924807936 | 5.6418600676 | 20.05775507287892 | 9.777926846037717 | 8.332931272727272 | 7.134547272727273 | 1.6895769999999999 | 5.3965136033057854 | 3.848816256198347 | 2.33152167768595 | 69.43774359599615 | 50.90176478678017 | 2.8546704389289994 | 32.19524319849596 | 19.23855199524021 | 7.38217409989887 | 5.674085230105022 | 4.386177378451562 | 2.7170156605913904 |
| user_002 | Jogging | 1.446034901754e12 | -1.80046 | -4.75112 | -0.345482 | -7.733138636363637 | -10.610643636363637 | 0.5846561818181819 | 3.2416562115999996 | 22.573141254400003 | 0.119357812324 | 5.092558814419722 | 13.50282014837676 | 5.932678636363637 | 5.859523636363637 | 0.930138181818182 | 4.5943209504132225 | 3.3978410743801657 | 0.9003685454545457 | 35.1966758023655 | 34.334017245104135 | 0.8651570372760333 | 29.45408911006125 | 14.127866162308719 | 1.2462307041419791 | 5.427162159919423 | 3.758705383813517 | 1.1163470357115564 |
| user_002 | Jogging | 1.446034901778e12 | -2.87342 | 0.958607 | -3.48775 | -4.932271363636365 | -6.635782363636365 | -0.19916863636363624 | 8.2565424964 | 0.918927380449 | 12.1644000625 | 4.619509707679918 | 9.170021680842996 | 2.058851363636365 | 7.594389363636365 | 3.288581363636364 | 4.947438099173554 | 5.119720438016529 | 1.120473223140496 | 4.238868937547319 | 57.674749806513155 | 10.814767385256406 | 30.827081428873772 | 29.06976515844823 | 2.2164938726372085 | 5.5522141014980475 | 5.391638448416978 | 1.488789398349279 |
| user_002 | Jogging | 1.446034901818e12 | -4.9044 | 4.25415 | -4.02423 | -2.5459604545454546 | -0.9399914545454545 | -3.32749890909091 | 24.05313936 | 18.0977922225 | 16.1944270929 | 7.6384133611241545 | 6.456276805127437 | 2.3584395454545453 | 5.194141454545455 | 0.6967310909090902 | 3.4811313636363637 | 5.85255605785124 | 2.658035719008265 | 5.562237089563842 | 26.97910544982757 | 0.48543421303937095 | 19.643839108268764 | 36.150759753165694 | 9.796203638719605 | 4.432137081394117 | 6.012550187163987 | 3.1298887582020556 |
| user_002 | Jogging | 1.44603490198e12 | -19.58108 | -9.00468 | -3.56439 | -8.276589090909091 | -8.83745909090909 | -2.00719 | 383.4186939664 | 81.0842619024 | 12.7048760721 | 21.845087135118046 | 13.987212825794312 | 11.304490909090909 | 0.1672209090909096 | 1.5572 | 6.14391758677686 | 4.873330545454544 | 3.3848551074380167 | 127.791514713719 | 2.796283243719025e-2 | 2.42487184 | 63.11184264424193 | 39.816429316796594 | 13.797553939075534 | 7.944296233414382 | 6.310026094779371 | 3.7145058808777693 |
| user_002 | Jogging | 1.446034902086e12 | -12.18526 | -5.82409 | -3.90927 | -15.679373636363636 | -11.188933636363636 | -7.281458181818183 | 148.4805612676 | 33.9200243281 | 15.282391932899998 | 14.059977863730795 | 20.738969766811962 | 3.494113636363636 | 5.364843636363636 | 3.3721881818181836 | 1.291173578512396 | 2.5475080991735535 | 2.0585292561983475 | 12.208830103822311 | 28.7815472426314 | 11.371653133594227 | 3.1528208578917405 | 9.549293706104132 | 6.302081652987829 | 1.7756184437800089 | 3.0901931502907924 | 2.5103947205544848 |
| user_002 | Jogging | 1.446034902207e12 | 1.57173 | 1.22685 | -1.15021 | -1.2779061818181818 | 1.5125097272727273 | -0.7844235454545454 | 2.4703351929000004 | 1.5051609225 | 1.3229830441 | 2.3018425574960597 | 3.8283394853389723 | 2.849636181818182 | 0.28565972727272726 | 0.36578645454545455 | 5.201743809917356 | 3.6169951900826445 | 1.362746347107438 | 8.120426368727308 | 8.160147978552892e-2 | 0.13379973032893389 | 28.055339392748365 | 15.483652908929882 | 3.2315638527796566 | 5.296729122085475 | 3.9349273066893984 | 1.7976550983933643 |
| user_002 | Jogging | 1.446034902215e12 | 0.460442 | 0.268841 | -1.57173 | -0.6752323636363635 | 1.7180461818181814 | -0.8889333636363635 | 0.21200683536400003 | 7.2275483281e-2 | 2.4703351929000004 | 1.6597040433598396 | 3.3886435757782847 | 1.1356743636363635 | 1.4492051818181815 | 0.6827966363636365 | 4.750450900826447 | 3.344636074380165 | 1.1093888181818181 | 1.2897562602208592 | 2.1001956590086683 | 0.4662112466294961 | 24.78997763030444 | 13.878270058378378 | 2.179490103587765 | 4.978953467376899 | 3.7253550244746307 | 1.4763096232117994 |
| user_002 | Jogging | 1.446034902231e12 | -1.57053 | -0.344284 | -1.11189 | -0.11784572727272725 | 1.8573923636363632 | -1.1467244545454547 | 2.4665644809 | 0.11853147265599999 | 1.2362993721000002 | 1.9548389513348663 | 2.9538514725909635 | 1.4526842727272726 | 2.201676363636363 | 3.483445454545464e-2 | 3.776608099173554 | 2.949714570247934 | 0.648277909090909 | 2.110291596229165 | 4.847378810195039 | 1.2134392234793455e-3 | 17.801429361908134 | 11.44855536925793 | 0.6513529155226738 | 4.219174014177199 | 3.383571392664551 | 0.8070643812749227 |
| user_002 | Jogging | 1.446034902514e12 | -2.91174 | -7.28026 | -1.53341 | -13.742459090909092 | -11.533813636363638 | -2.3033 | 8.4782298276 | 53.0021856676 | 2.3513462280999997 | 7.989478188423822 | 18.360166618428106 | 10.830719090909092 | 4.2535536363636375 | 0.7698900000000002 | 6.058093305785124 | 2.7144082644628105 | 0.724285438016529 | 117.30447602618267 | 18.092718537422325 | 0.5927306121000003 | 46.810740975525555 | 8.797815719086705 | 0.7294053996474156 | 6.841837543783509 | 2.966111211516976 | 0.8540523401100284 |
| user_002 | Jogging | 1.446034902531e12 | -2.33694 | -4.55952 | -1.61005 | -10.610644545454544 | -9.711857272727274 | -2.2823972727272728 | 5.461288563599999 | 20.7892226304 | 2.5922610025 | 5.370546731618672 | 14.921223269515052 | 8.273704545454544 | 5.152337272727274 | 0.6723472727272728 | 6.659500743801653 | 3.3094812396694224 | 0.8186608925619836 | 68.45418690547518 | 26.546579371934723 | 0.4520508551438017 | 55.78962539186213 | 12.58307267179482 | 0.8407906514539085 | 7.469245302697062 | 3.5472627012662623 | 0.916946373270492 |
| user_002 | Jogging | 1.446034902627e12 | -2.68182 | -0.535886 | -1.68669 | -4.650093636363636 | 2.359038545454545 | -3.0940923636363635 | 7.192158512400001 | 0.287173804996 | 2.8449231561 | 3.213137948096222 | 6.405242851026029 | 1.9682736363636355 | 2.894924545454545 | 1.4074023636363635 | 0.9624421487603303 | 4.815373719008264 | 1.4479379008264461 | 3.8741011076041287 | 8.380588123875205 | 1.9807814131692227 | 1.8753445041277237 | 29.04896108408565 | 3.2992674363277446 | 1.3694321831064595 | 5.389708812550605 | 1.8163885697525584 |
| user_002 | Jogging | 1.446034902684e12 | -0.191003 | -8.65979 | 1.45557 | -1.7203319090909088 | -2.8560078181818183 | -1.2163973636363639 | 3.6482146009e-2 | 74.99196284409999 | 2.1186840249000003 | 8.783343840190307 | 5.8018233301148125 | 1.5293289090909088 | 5.803782181818181 | 2.671967363636364 | 1.8909967272727266 | 4.315626595041322 | 2.0445943223140493 | 2.338846912181189 | 33.68388761399021 | 7.139409592337862 | 4.752487980665275 | 22.310358190105372 | 4.801100736618481 | 2.180020178958276 | 4.723384188281255 | 2.1911414232354973 |
| user_002 | Jogging | 1.446034902701e12 | -2.41358 | -8.46819 | 0.727487 | -1.2361019090909091 | -5.099487818181818 | -0.15039581818181824 | 5.8253684164 | 71.7102418761 | 0.529237335169 | 8.8354313775655 | 6.140365980333311 | 1.177478090909091 | 3.3687021818181817 | 0.8778828181818182 | 2.0145087107438013 | 4.656392776859504 | 2.0490282396694215 | 1.3864546545709175 | 11.348154389786577 | 0.7706782424588513 | 4.8853431088144275 | 24.57087881120827 | 4.879751059280171 | 2.210281228444568 | 4.956902138554711 | 2.2090158576343835 |
| user_002 | Jogging | 1.446034902805e12 | -17.62674 | -9.08132 | -6.40009 | -15.128957272727273 | -12.470921818181818 | -6.333900909090909 | 310.7019630276001 | 82.47037294239999 | 40.961152008099994 | 20.835870223681564 | 20.78196857637037 | 2.497782727272728 | 3.389601818181818 | 6.618909090909053e-2 | 6.036874694214876 | 2.4832193388429755 | 3.284779181818182 | 6.238918552661988 | 11.489400485821488 | 4.3809957553718505e-3 | 41.12672443825338 | 7.965350005252444 | 13.045420306964319 | 6.413012118985383 | 2.8222951662171063 | 3.6118444466732393 |
| user_002 | Jogging | 1.446034902968e12 | 4.63736 | -0.919089 | -3.2195 | -0.7205193636363639 | 0.31064381818181813 | -0.2026514545454545 | 21.505107769600002 | 0.8447245899210001 | 10.36518025 | 5.719703891769311 | 4.442509944887756 | 5.357879363636364 | 1.2297328181818181 | 3.0168485454545455 | 6.339320231404958 | 2.474667537190083 | 3.0231858347107434 | 28.70687127528041 | 1.5122428041133966 | 9.101375146211208 | 40.40514044965991 | 6.740364931990786 | 10.65622385603228 | 6.3565037913667535 | 2.5962212794734554 | 3.2643872098806357 |
| user_002 | Jogging | 1.446034902976e12 | 5.67201 | -2.8351 | -2.52974 | 0.4534760909090909 | 0.2374874545454545 | -0.5266323636363637 | 32.171697440100004 | 8.03779201 | 6.3995844675999995 | 6.827083851667563 | 4.372978499695182 | 5.218533909090909 | 3.0725874545454546 | 2.0031076363636364 | 6.274081082644628 | 2.4797344132231407 | 2.742275867768595 | 27.233096160331645 | 9.440793665830116 | 4.012440202858314 | 39.67741741780719 | 6.7712193658965285 | 8.662823551405499 | 6.2990013032072945 | 2.6021566758933883 | 2.94326749572741 |
| user_002 | Jogging | 1.446034903e12 | 6.20849 | -3.71647 | -3.0279 | 3.466846090909091 | -0.5637553636363637 | -1.5125087272727271 | 38.545348080100005 | 13.812149260900002 | 9.16817841 | 7.843830425946242 | 5.121713069249923 | 2.7416439090909095 | 3.1527146363636365 | 1.5153912727272727 | 5.613451404958678 | 2.6273143719008263 | 1.9939214214876033 | 7.516611324255283 | 9.939609578341496 | 2.2964107094579833 | 33.31595687994492 | 7.729724814435821 | 4.668799177641327 | 5.771997650722401 | 2.7802382657671303 | 2.1607404234755565 |
| user_002 | Jogging | 1.446034903122e12 | -15.05928 | -10.46085 | 0.229323 | -10.676833636363638 | -10.809214545454546 | 0.23977427272727272 | 226.78191411839998 | 109.42938272250001 | 5.2589038329e-2 | 18.337499444559747 | 15.370729188349905 | 4.382446363636362 | 0.34836454545454565 | 1.0451272727272726e-2 | 7.050937512396694 | 3.9726451900826447 | 1.4612389256198348 | 19.20583613014957 | 0.1213578565297522 | 1.0922910161983468e-4 | 50.68204593155306 | 22.592195796465415 | 2.9680132535840995 | 7.119132386151635 | 4.7531248454532955 | 1.722792283934456 |
| user_002 | Jogging | 1.446034903162e12 | -15.82569 | -12.79839 | -0.345482 | -13.836517272727274 | -12.927282727272729 | 0.260676 | 250.4524639761 | 163.7987865921 | 0.119357812324 | 20.35609511621824 | 19.101228338167484 | 1.9891727272727255 | 0.1288927272727296 | 0.606158 | 4.112941603305784 | 2.917411074380166 | 1.1961640578512398 | 3.956808138925613 | 1.6613335143802255e-2 | 0.36742752096399994 | 25.41021847834294 | 18.272074295056942 | 2.3195890725029122 | 5.040854935260778 | 4.274584692699039 | 1.5230197216395172 |
| user_002 | Jogging | 1.446034903307e12 | -1.53221 | -2.49022 | -1.30349 | -5.392114545454546 | -6.336188181818182 | -2.083830909090909 | 2.3476674841 | 6.2011956484 | 1.6990861801000001 | 3.2012418391305584 | 8.739578917812501 | 3.8599045454545458 | 3.845968181818182 | 0.780340909090909 | 5.506407685950413 | 3.3297501652892563 | 1.055867 | 14.898863100020664 | 14.791471255557852 | 0.6089319344008264 | 31.50156618148407 | 11.305911970917434 | 1.225309888233371 | 5.612625604962803 | 3.362426500448364 | 1.106937165440465 |
| user_002 | Jogging | 1.446034903364e12 | -3.06503 | 3.90927 | -2.52974 | -2.817686363636364 | 0.32109581818181826 | -3.007001818181818 | 9.3944089009 | 15.282391932899998 | 6.3995844675999995 | 5.574619745005035 | 5.520307117261362 | 0.24734363636363632 | 3.5881741818181814 | 0.47726181818181823 | 2.2561485950413216 | 4.613006 | 1.049850388429752 | 6.117887444958676e-2 | 12.874993959066575 | 0.22777884309421492 | 10.156513458621633 | 22.844478304031163 | 1.672854066725234 | 3.186928530516747 | 4.779589763152394 | 1.2933885984982372 |
| user_002 | Jogging | 1.446034903389e12 | -0.114362 | -2.29862 | 1.18733 | -2.5982147272727274 | 1.533411636363636 | -2.2580138181818183 | 1.3078667044000002e-2 | 5.2836539044 | 1.4097525289 | 2.5896882245444144 | 4.741289185702595 | 2.4838527272727275 | 3.832031636363636 | 3.4453438181818186 | 1.128391652892562 | 4.422671603305786 | 1.6686763140495868 | 6.169524370780167 | 14.684466462091768 | 11.870394025483671 | 2.6154585313649137 | 22.066950396758074 | 4.132894945850269 | 1.6172379328240214 | 4.697547274563405 | 2.0329522733823016 |
| user_002 | Jogging | 1.446034903453e12 | -4.06135 | -6.59049 | 0.842448 | -0.9260550909090909 | -2.514606 | 1.138559636363636 | 16.4945638225 | 43.4345584401 | 0.7097186327039999 | 7.787094509205857 | 3.6863808338969126 | 3.1352949090909092 | 4.075884 | 0.29611163636363613 | 1.7944037190082645 | 3.1096454049586773 | 2.334055776859504 | 9.830074166971373 | 16.612830381456003 | 8.768210118995028e-2 | 4.132053315964243 | 10.482384637933924 | 6.663167319451804 | 2.032745265881646 | 3.2376510988576155 | 2.581311162849571 |
| user_002 | Jogging | 1.446034903559e12 | -19.58108 | -10.42253 | -8.81427 | -15.08018545454545 | -16.330821818181818 | -7.13166 | 383.4186939664 | 108.6291316009 | 77.69135563290001 | 23.86920989895141 | 23.72028927423204 | 4.50089454545455 | 5.908291818181818 | 1.6826100000000004 | 5.996020479338842 | 5.813287438016529 | 3.578356950413223 | 20.258051709302517 | 34.90791220879421 | 2.8311764121000014 | 37.15895341889142 | 43.90340861610187 | 14.47403434508205 | 6.095814418015974 | 6.625964730973284 | 3.80447556768105 |
| user_002 | Jogging | 1.446034903656e12 | -3.02671 | -2.10702 | 1.83878 | -12.244483636363638 | -4.859112727272729 | -3.811728818181818 | 9.1609734241 | 4.439533280399999 | 3.3811118884000004 | 4.120875949710206 | 13.973353179277263 | 9.217773636363638 | 2.752092727272729 | 5.650508818181818 | 5.437367190082646 | 4.621873553719009 | 3.1761519338842983 | 84.96735081124054 | 7.574014379507447 | 31.928249904350483 | 40.0247632765042 | 23.596950291099475 | 14.544418632169284 | 6.326512726337015 | 4.857669224134089 | 3.81371454518679 |
| user_002 | Jogging | 1.44603490385e12 | -13.52647 | -11.72542 | 2.91174 | -8.245234727272727 | -7.551986 | -0.9133200000000002 | 182.9653906609 | 137.4854741764 | 8.4782298276 | 18.13640247306229 | 12.780088083552192 | 5.2812352727272724 | 4.173433999999999 | 3.82506 | 6.257928099173554 | 4.886315537190083 | 2.9636499669421483 | 27.891446005898707 | 17.417551352355996 | 14.631084003600002 | 44.13147140172497 | 40.37989817122545 | 16.726569752810427 | 6.643152218768209 | 6.354517933818855 | 4.089812923938016 |
| user_002 | Jogging | 1.446034903963e12 | -10.38421 | -11.84038 | 0.842448 | -14.96870909090909 | -12.693877272727274 | -1.4532909090909076e-2 | 107.83181732409999 | 140.19459854439998 | 0.7097186327039999 | 15.771370723599263 | 19.95964069919205 | 4.584499090909091 | 0.8534972727272745 | 0.8569809090909091 | 3.128014958677686 | 1.8362072727272731 | 2.8122667933884298 | 21.017631914546282 | 0.7284575945528956 | 0.734416278546281 | 11.367615322103454 | 4.033308503247484 | 12.47566986144667 | 3.3715894355783376 | 2.0083098623587654 | 3.5320914288062633 |
| user_002 | Jogging | 1.4460349041e12 | -2.29862 | 5.67201 | -5.51872 | -3.556223636363637 | -0.1143610909090908 | -2.2336276363636363 | 5.2836539044 | 32.171697440100004 | 30.4562704384 | 8.24085079241822 | 5.5614694400953235 | 1.257603636363637 | 5.786371090909091 | 3.285092363636364 | 3.1761520661157023 | 4.962956611570248 | 1.373829479338843 | 1.5815669061950428 | 33.48209040170846 | 10.791831837621952 | 11.354773319808565 | 25.18833329477783 | 2.847330828690515 | 3.3696844540414412 | 5.018797993023611 | 1.6874035761164294 |
| user_002 | Jogging | 1.446034904166e12 | -1.14901 | -2.72014 | 1.57053 | -3.9254900000000004 | 1.7180463636363634 | -1.6866922727272722 | 1.3202239801000002 | 7.399161619599999 | 2.4665644809 | 3.34454033920956 | 6.142751883178885 | 2.7764800000000003 | 4.438186363636364 | 3.2572222727272724 | 2.130101818181818 | 4.048336429752066 | 2.1747557190082643 | 7.708841190400001 | 19.697498198367768 | 10.609496933950618 | 5.225022162017731 | 19.2436754831257 | 5.679544464708638 | 2.2858307378320317 | 4.386761388897931 | 2.3831794864652216 |
| user_002 | Jogging | 1.446034904303e12 | -19.58108 | -12.87503 | -9.84892 | -13.954961818181816 | -12.484857272727272 | -6.87387 | 383.4186939664 | 165.7663975009 | 97.00122516639999 | 25.420195054989254 | 20.16898227588495 | 5.626118181818184 | 0.3901727272727289 | 2.9750499999999995 | 6.916658239669422 | 4.181031487603305 | 3.943508214876033 | 31.653205795785144 | 0.15223475710743928 | 8.850922502499998 | 49.76640175867421 | 23.384981400124193 | 16.336958474720095 | 7.054530583864118 | 4.835802043107657 | 4.041900354377888 |
| user_002 | Jogging | 1.44603490436e12 | -15.02096 | -6.20729 | -7.70298 | -18.717131818181816 | -9.833787272727273 | -8.61221909090909 | 225.6292393216 | 38.530449144100004 | 59.335900880400004 | 17.98598313537795 | 23.016621933143078 | 3.6961718181818153 | 3.6264972727272724 | 0.9092390909090895 | 4.279207363636366 | 3.1048967768595035 | 2.2276457851239666 | 13.661686109521467 | 13.151482469098346 | 0.8267157244371875 | 24.770207500773985 | 11.33453922324981 | 8.910379694181366 | 4.976967701399516 | 3.366680742697443 | 2.9850259118107108 |
| user_002 | Jogging | 1.446034904367e12 | -12.68342 | -5.51753 | -6.70665 | -18.309542727272728 | -9.070864545454546 | -8.340492727272725 | 160.8691428964 | 30.4431373009 | 44.9791542225 | 15.37177395162315 | 22.220551075809762 | 5.626122727272728 | 3.553334545454546 | 1.6338427272727252 | 4.0572033057851264 | 3.084944214876033 | 1.8875136363636358 | 31.653256942334718 | 12.626186391920665 | 2.6694420574619766 | 21.730019531200156 | 11.188363815690682 | 6.526346154230428 | 4.661546903250053 | 3.3449011668045863 | 2.554671437627631 |
| user_002 | Jogging | 1.446034904376e12 | -10.95901 | -5.13432 | -5.82529 | -17.577972727272726 | -8.269620909090909 | -7.870198181818181 | 120.09990018009998 | 26.361241862399996 | 33.9340035841 | 13.431125999952497 | 21.076610019864965 | 6.618962727272727 | 3.135300909090909 | 2.0449081818181813 | 3.907088595041323 | 3.1112299999999995 | 1.5556150413223135 | 43.81066758502562 | 9.830111790546281 | 4.18164947206694 | 19.49493349054441 | 11.345591140185949 | 3.957218397868069 | 4.4153067266662696 | 3.3683217097222093 | 1.989275847605874 |
| user_002 | Jogging | 1.446034904401e12 | -7.7401 | -3.48655 | -2.8363 | -14.588988181818182 | -5.907696363636364 | -6.295580909090909 | 59.90914801 | 12.1560309025 | 8.04459769 | 8.950406504874513 | 16.99071880628637 | 6.848888181818182 | 2.4211463636363644 | 3.4592809090909094 | 4.26622305785124 | 3.505834132231405 | 1.504627107438016 | 46.907269327048766 | 5.86194971414959 | 11.966624408000829 | 23.70208991383614 | 12.767608535055674 | 3.195596680140795 | 4.868479219821745 | 3.5731790516367457 | 1.7876231929970015 |
| user_002 | Jogging | 1.446034904481e12 | 2.56806 | 1.26517 | -1.87829 | -2.8107182727272733 | -1.194297818181818 | 0.2989964545454545 | 6.5949321636 | 1.6006551288999997 | 3.5279733241 | 3.4239685478403565 | 4.608302892383878 | 5.378778272727273 | 2.459467818181818 | 2.1772864545454547 | 5.678689809917356 | 2.3445062148760334 | 3.615093132231405 | 28.93125570716299 | 6.048981948672032 | 4.740576305147116 | 32.508980915233295 | 5.547095110902433 | 15.245666932265921 | 5.701664749459871 | 2.355227188808424 | 3.9045700060654465 |
| user_002 | Jogging | 1.446034904538e12 | 2.72134 | -2.6435 | -3.41111 | 3.4424599090909087 | -0.6090423636363635 | -1.4950904545454546 | 7.405691395600001 | 6.98809225 | 11.635671432099999 | 5.101907004023103 | 4.55478708374157 | 0.7211199090909086 | 2.034457636363636 | 1.9160195454545452 | 4.419503983471075 | 1.9660537438016528 | 2.015458099173554 | 0.5200139232872804 | 4.139017874158314 | 3.6711308985638422 | 22.016640849837806 | 4.48710159313118 | 4.649995244198693 | 4.692189345053949 | 2.1182779782481758 | 2.1563847625594774 |
| user_002 | Jogging | 1.446034904765e12 | -13.10495 | -10.1926 | -1.95493 | -14.696981818181818 | -12.634653636363636 | -0.47089309090909093 | 171.73971450250002 | 103.88909476 | 3.8217513049000003 | 16.7167748255278 | 19.43928532597381 | 1.5920318181818178 | 2.442053636363635 | 1.4840369090909091 | 2.847736140495868 | 3.617310611570248 | 0.5342670909090909 | 2.5345653101033045 | 5.963625962876853 | 2.2023655475440993 | 10.064474543588254 | 16.68699578470033 | 0.410335260510263 | 3.172455601515686 | 4.084971944175423 | 0.6405741647227611 |
| user_002 | Jogging | 1.446034904773e12 | -12.03198 | -9.19628 | -2.8363 | -14.679563636363637 | -12.31067272727273 | -0.6590108181818182 | 144.7685427204 | 84.57156583839999 | 8.04459769 | 15.407293930109855 | 19.232055710552334 | 2.6475836363636365 | 3.1143927272727296 | 2.177289181818182 | 2.7045890909090913 | 3.2676766776859507 | 0.6831144380165289 | 7.009699111540496 | 9.699442059689272 | 4.7405881812624875 | 9.081085115569495 | 13.164518257948806 | 0.8147918375588407 | 3.013483883409615 | 3.628294125060537 | 0.9026582063875788 |
| user_002 | Jogging | 1.446034904789e12 | -10.38421 | -7.20362 | -2.22318 | -14.254558181818181 | -11.439757272727274 | -1.052665090909091 | 107.83181732409999 | 51.8921411044 | 4.9425293124000005 | 12.83224406488982 | 18.390656255115662 | 3.8703481818181817 | 4.236137272727274 | 1.170514909090909 | 2.6713357024793383 | 3.084626363636364 | 0.8677489999999999 | 14.979595048503306 | 17.944858993389268 | 1.370105152404099 | 8.862194919781967 | 11.457581575641553 | 1.217527360808707 | 2.976943889256559 | 3.384904958140118 | 1.1034162228319406 |
| user_002 | Jogging | 1.446034904813e12 | -5.82409 | -7.24194 | -0.690364 | -11.965790909090911 | -9.454068181818185 | -1.383612909090909 | 33.9200243281 | 52.4456949636 | 0.476602452496 | 9.318922778100267 | 15.389952193371483 | 6.141700909090911 | 2.2121281818181853 | 0.6932489090909091 | 3.0798742148760336 | 2.510138595041323 | 0.8997350330578513 | 37.720490056728124 | 4.89351109279423 | 0.4805940499557355 | 12.78673982575342 | 7.319570001085881 | 1.251892622913894 | 3.575855118115584 | 2.7054703844407317 | 1.1188800753047192 |
| user_002 | Jogging | 1.44603490487e12 | -3.25663 | 4.5224 | -4.67568 | -6.130652727272727 | -2.486738545454545 | -2.303301818181818 | 10.6056389569 | 20.45210176 | 21.861983462399998 | 7.274594434007988 | 8.44300942622068 | 2.874022727272727 | 7.009138545454546 | 2.3723781818181817 | 4.0055809090909085 | 5.034529355371902 | 0.8322801074380163 | 8.260006636880163 | 49.128023149376666 | 5.628178237566941 | 18.04848911271097 | 29.640567436933203 | 1.0455075809433545 | 4.248351340545056 | 5.4443151485685695 | 1.0225006508278391 |
| user_002 | Jogging | 1.446034904894e12 | -0.804128 | 1.34181 | -2.0699 | -4.075289818181819 | 0.38380145454545445 | -2.7352758181818184 | 0.646621840384 | 1.8004540760999999 | 4.28448601 | 2.5945253759568434 | 6.449416857876946 | 3.271161818181819 | 0.9580085454545455 | 0.6653758181818183 | 3.617944710743801 | 5.087417801652894 | 0.9158868429752065 | 10.700499640730584 | 0.917780373163934 | 0.4427249794211242 | 14.79149537720143 | 30.924407254086645 | 1.2617954625082854 | 3.84597131778195 | 5.560971790441545 | 1.1232966938918165 |
| user_002 | Jogging | 1.446034904942e12 | -0.957409 | -2.4519 | -1.22685 | -1.6854947272727272 | 0.575403727272727 | -1.5543139090909088 | 0.9166319932809999 | 6.011813610000001 | 1.5051609225 | 2.9040672385089503 | 3.9559630267711743 | 0.7280857272727272 | 3.0273037272727272 | 0.32746390909090883 | 2.25044847107438 | 3.949843132231405 | 1.8406406776859507 | 0.5301088262582562 | 9.164567857159346 | 0.107232611757099 | 5.822947048104639 | 20.150232924354416 | 4.563080723516522 | 2.4130783344319013 | 4.488901082041619 | 2.136136869097231 |
| user_002 | Jogging | 1.446034905113e12 | -16.70706 | -5.51753 | -6.13185 | -18.264255454545456 | -11.896116363636365 | -8.065281818181818 | 279.12585384359994 | 30.4431373009 | 37.5995844225 | 18.632460266078656 | 23.416310510578196 | 1.5571954545454574 | 6.378586363636365 | 1.933431818181818 | 3.513750330578513 | 3.527368347107439 | 2.832853694214876 | 2.4248576836570335 | 40.68636399836778 | 3.7381585955578505 | 25.832541611438845 | 14.991027844016308 | 10.362856133310423 | 5.0825723419779125 | 3.8718248725912576 | 3.219139036032837 |
| user_002 | Jogging | 1.446034905169e12 | -7.89339 | 0.728685 | 1.37893 | -13.693688181818182 | -3.695569454545455 | -4.693093454545454 | 62.3056056921 | 0.530981829225 | 1.9014479449 | 8.045994995413867 | 15.34229944936368 | 5.800298181818182 | 4.424254454545455 | 6.0720234545454534 | 3.043455371900826 | 5.645120239669422 | 2.632384115702479 | 33.643458998003304 | 19.574027478565302 | 36.869468832550105 | 13.436308781997292 | 32.79240188340361 | 10.397747111488627 | 3.66555709026572 | 5.726465042537465 | 3.224553784865222 |
| user_002 | Jogging | 1.446034905493e12 | -15.44249 | -13.64143 | -0.11556 | -15.313590909090909 | -13.67975090909091 | 1.054952090909091 | 238.4704974001 | 186.08861244489998 | 1.3354113599999998e-2 | 20.60515624688636 | 21.085682903240833 | 0.12889909090909057 | 3.8320909090909794e-2 | 1.170512090909091 | 2.6067309917355375 | 1.5084261983471072 | 3.4773302066115708 | 1.6614975637189996e-2 | 1.468492073553773e-3 | 1.3700985549643718 | 14.628895633531561 | 3.0635775120513133 | 19.6558582853718 | 3.8247739323431342 | 1.7503078335113835 | 4.433492786209514 |
| user_002 | Jogging | 1.446034905501e12 | -14.906 | -12.10862 | -0.1922 | -15.111538181818183 | -13.700652727272727 | 0.25022572727272735 | 222.188836 | 146.6186783044 | 3.694084e-2 | 19.205323614675176 | 20.7796444806875 | 0.2055381818181825 | 1.592032727272727 | 0.44242572727272733 | 1.8586934710743805 | 1.4092996694214877 | 2.8122669752066116 | 4.224594418512425e-2 | 2.5345682047074374 | 0.19574052415280171 | 8.166233634188808 | 2.6398653995649872 | 14.201975998263556 | 2.857662267341753 | 1.624766259978643 | 3.768550914909278 |
| user_002 | Jogging | 1.446034905631e12 | -1.34061 | 4.40743 | -4.13919 | -5.099487272727273 | -2.1732090909090913 | -2.3729745454545452 | 1.7972351721000002 | 19.425439204899998 | 17.1328938561 | 6.193187243503946 | 7.331724758582661 | 3.758877272727273 | 6.5806390909090915 | 1.766215454545455 | 4.159495123966942 | 4.7365159504132235 | 0.7170017685950415 | 14.12915835142562 | 43.30481084480083 | 3.119517031875208 | 17.9341842130411 | 25.112124580614953 | 0.9352357872741565 | 4.234877119001341 | 5.011199914253567 | 0.9670758953019957 |
| user_002 | Jogging | 1.446034905663e12 | -2.18366 | -0.420925 | -3.29615 | -2.772398818181818 | 0.8157759090909089 | -3.243890909090909 | 4.768370995600001 | 0.177177855625 | 10.864604822499999 | 3.976198394663551 | 5.5856101440878625 | 0.5887388181818181 | 1.236700909090909 | 5.2259090909090755e-2 | 3.2746450495867765 | 4.383083636363636 | 1.011529834710744 | 0.34661339603412383 | 1.5294291385462806 | 2.731012582644612e-3 | 12.606584914941841 | 23.25747815560631 | 1.7777824431994913 | 3.5505752935181984 | 4.822600766765409 | 1.333335082865328 |
| user_002 | Jogging | 1.446034905719e12 | -0.229323 | -3.33327 | 4.02303 | -1.9885736363636364 | -1.3545477272727275 | -0.1956850909090907 | 5.2589038329e-2 | 11.1106888929 | 16.1847703809 | 5.229536146937796 | 5.172272803925215 | 1.7592506363636364 | 1.9787222727272726 | 4.218715090909091 | 1.1581613553719008 | 3.279396074380165 | 3.183435008264463 | 3.0949628015458597 | 3.915341832586983 | 17.797557018264104 | 2.2964233602852517 | 12.828385137281272 | 12.516466305779879 | 1.515395446834011 | 3.581673510704357 | 3.537861826835508 |
| user_002 | Jogging | 1.446034905809e12 | -19.58108 | -11.72542 | -10.23212 | -11.652259727272728 | -11.49201090909091 | -4.919532454545455 | 383.4186939664 | 137.4854741764 | 104.6962796944 | 25.012006073827827 | 18.2206364433844 | 7.928820272727272 | 0.23340909090909 | 5.312587545454545 | 7.430340818181818 | 4.184197314049587 | 4.264320454545454 | 62.86619091721097 | 5.447980371900785e-2 | 28.22358642811875 | 69.60503321583221 | 22.851830057978496 | 21.710520234285465 | 8.34296309567723 | 4.780358779210876 | 4.6594549288822895 |
| user_002 | Jogging | 1.44603490589e12 | -10.72909 | -2.33694 | -6.05521 | -16.79414909090909 | -7.332516363636364 | -7.96774 | 115.11337222809999 | 5.461288563599999 | 36.6655681441 | 12.539546600088856 | 20.12150734565 | 6.065059090909092 | 4.9955763636363635 | 1.9125300000000003 | 3.6692487272727266 | 3.119780082644628 | 2.1247186446281 | 36.784941776219014 | 24.955783204922312 | 3.6577710009000013 | 18.701081109706283 | 13.284676463152287 | 7.08943210848389 | 4.324474662858632 | 3.644815010827338 | 2.6625987509356137 |
| user_002 | Jogging | 1.44603490593e12 | -4.82776 | -0.574206 | -5.99e-4 | -11.269057272727274 | -3.068510363636363 | -4.494526545454545 | 23.307266617599996 | 0.329712530436 | 3.5880100000000004e-7 | 4.861787686318377 | 12.660603854836262 | 6.441297272727274 | 2.4943043636363633 | 4.493927545454545 | 4.729232892561983 | 4.135426595041323 | 2.612748289256199 | 41.49031055564382 | 6.221554258455403 | 20.19538478379511 | 26.98205981281337 | 18.009014885965737 | 9.086475011597456 | 5.19442584053458 | 4.243702968630785 | 3.0143780472259043 |
| user_002 | Jogging | 1.446034906132e12 | -15.74905 | -17.81835 | 0.344284 | -8.572700272727273 | -9.20672818181818 | -1.6274719090909096 | 248.0325759025 | 317.49359672249994 | 0.11853147265599999 | 23.78328623419514 | 13.337573546264622 | 7.1763497272727275 | 8.611621818181819 | 1.9717559090909096 | 7.523449586776858 | 4.22220267768595 | 1.909365611570248 | 51.49999540812735 | 74.16003033938513 | 3.8878213650349194 | 61.7491867891929 | 25.090541422510398 | 5.45840222276125 | 7.858065079215932 | 5.009045959313051 | 2.336322371326622 |
| user_002 | Jogging | 1.446034906521e12 | -16.36218 | -11.4955 | -7.5497 | -4.873049363636364 | -7.747071818181819 | -0.7495861818181818 | 267.7209343524 | 132.14652025 | 56.997970089999995 | 21.374410510991876 | 10.57141105888111 | 11.489130636363635 | 3.7484281818181806 | 6.800113818181818 | 4.584187264462809 | 4.209851421487603 | 3.757607553719008 | 132.00012277942946 | 14.050713834248752 | 46.2415479402273 | 38.234762361468455 | 24.94539292302512 | 16.971525438143534 | 6.183426425653374 | 4.994536307108511 | 4.119651130635158 |
| user_002 | Jogging | 1.446034906529e12 | -17.12858 | -10.46085 | -8.27779 | -6.38147609090909 | -8.294007272727272 | -1.606568 | 293.3882528164 | 109.42938272250001 | 68.52180728409999 | 21.710353355553657 | 12.124911226676351 | 10.74710390909091 | 2.166842727272728 | 6.671221999999999 | 5.3670615123966945 | 4.060687322314049 | 4.055619611570248 | 115.50024243279712 | 4.695207404734715 | 44.50520297328399 | 48.32021117320367 | 24.054213422791477 | 19.97081157847642 | 6.951274068341981 | 4.904509498695203 | 4.468871398740002 |
| user_002 | Jogging | 1.446034906715e12 | 1.99325 | -0.344284 | -0.1922 | -6.9562799090909095 | -0.9434740909090908 | -2.592445181818182 | 3.9730455625 | 0.11853147265599999 | 3.694084e-2 | 2.031875457589859 | 7.956944177253901 | 8.94952990909091 | 0.5991900909090908 | 2.400245181818182 | 6.556257595041322 | 2.1237692975206617 | 3.3148641404958683 | 80.09408559371273 | 0.3590287650436445 | 5.761176932841397 | 47.557804477929864 | 6.570856032718509 | 11.231214085224345 | 6.896216678580355 | 2.563368103242004 | 3.351300357357476 |
| user_002 | Jogging | 1.446034906764e12 | 5.55704 | 3.8919e-2 | -1.57173 | 1.648371909090909 | 0.29671036363636366 | -1.0456967272727273 | 30.880693561599998 | 1.514688561e-3 | 2.4703351929000004 | 5.775166096577742 | 3.927382842329911 | 3.9086680909090905 | 0.2577913636363637 | 0.5260332727272727 | 7.148796636363636 | 0.9941112479338842 | 1.907780231404959 | 15.277686244890914 | 6.645638716549589e-2 | 0.2767110040161653 | 53.543776224592506 | 1.218222511930528 | 5.9435548466332 | 7.317361288373871 | 1.103731177384479 | 2.4379406979320066 |
| user_002 | Jogging | 1.446034906788e12 | 3.41111 | -2.60518 | -3.8919e-2 | 3.8744337272727276 | -0.14223127272727273 | -1.0038927272727272 | 11.635671432099999 | 6.7869628323999995 | 1.514688561e-3 | 4.2923360717750185 | 4.281714191828106 | 0.46332372727272775 | 2.4629487272727273 | 0.9649737272727271 | 5.373076958677687 | 1.2151654049586775 | 1.0077278181818183 | 0.214668876253893 | 6.066116433174347 | 0.9311742943266196 | 36.83587198966369 | 1.9644262833778166 | 2.035507535520235 | 6.069256296257696 | 1.4015799240064108 | 1.426712141786224 |
| user_002 | Jogging | 1.446034906869e12 | -9.15796 | -6.59049 | -4.59904 | -4.7615704545454545 | -3.653766363636364 | -1.1258220909090908 | 83.86823136159998 | 43.4345584401 | 21.151168921599997 | 12.184168364041101 | 7.534283163932319 | 4.396389545454545 | 2.9367236363636358 | 3.4732179090909088 | 5.570380008264463 | 1.7925042231404957 | 1.193629347107438 | 19.328241035382018 | 8.624345716376856 | 12.063242644029824 | 39.045948739142915 | 4.770132105713073 | 2.186160728735234 | 6.2486757588422615 | 2.1840632101001733 | 1.4785671201319317 |
| user_002 | Jogging | 1.446034907055e12 | -8.96635 | -6.28393 | -2.37646 | -14.055988181818185 | -11.060037272727273 | -0.8680313636363636 | 80.3954323225 | 39.4877762449 | 5.647562131599999 | 11.204051530540191 | 17.980667829343247 | 5.089638181818184 | 4.776107272727273 | 1.5084286363636363 | 3.1850199173553735 | 1.969853553719008 | 0.9963280909090908 | 25.904416821821513 | 22.811200680598354 | 2.2753569510018594 | 12.3243425505523 | 5.979313249989634 | 1.477317282096447 | 3.5106042999108147 | 2.445263431614196 | 1.2154494156880602 |
| user_002 | Jogging | 1.446034907176e12 | -3.17999 | 2.10822 | -2.72134 | -3.253144545454545 | 1.756367909090909 | -2.9408118181818184 | 10.1123364001 | 4.444591568400001 | 7.405691395600001 | 4.686429276549472 | 5.4434715507149125 | 7.31545454545448e-2 | 0.3518520909090912 | 0.21947181818181827 | 2.009758181818181 | 3.9077233719008264 | 0.8763001735537188 | 5.3515875206610625e-3 | 0.12379989387709937 | 4.81678789760331e-2 | 8.371246828439217 | 17.596841194972075 | 1.1728786380655 | 2.893310703750846 | 4.194858900484268 | 1.0829952160861562 |
| user_002 | Jogging | 1.446034907217e12 | -2.60518 | -3.44823 | -0.1922 | -3.1695363636363636 | 1.3557463636363638 | -2.0873139999999997 | 6.7869628323999995 | 11.8902901329 | 3.694084e-2 | 4.325990499908662 | 5.171482505680273 | 0.5643563636363638 | 4.803976363636364 | 1.8951139999999997 | 0.4522432231404956 | 3.8599021983471076 | 1.5062108264462812 | 0.31849810517685967 | 23.078188902376866 | 3.591457072995999 | 0.2748354196277234 | 17.748269714747806 | 2.709150374558628 | 0.5242474793718358 | 4.2128695345035085 | 1.645949687736119 |
| user_002 | Jogging | 1.446034907282e12 | -12.98999 | -15.71073 | -5.0972 | -5.245800909090909 | -6.998084545454546 | -1.1084039090909092 | 168.73984020010002 | 246.8270371329 | 25.98144784 | 21.01305130562908 | 9.044514809492759 | 7.744189090909091 | 8.712645454545454 | 3.9887960909090907 | 2.1218685123966945 | 5.465871355371901 | 2.3274055785123964 | 59.972464675755376 | 75.91019081661156 | 15.910494254851644 | 12.08835882428768 | 34.59527934232356 | 6.941450569358176 | 3.4768317221700102 | 5.881775186312679 | 2.634663274378374 |
| user_002 | Jogging | 1.44603490737e12 | -19.15956 | -7.85507 | -7.05154 | -18.156263636363633 | -11.809024545454546 | -8.901361818181819 | 367.08873939359995 | 61.70212470490001 | 49.7242163716 | 21.874987553598743 | 23.72199698592232 | 1.003296363636366 | 3.953954545454546 | 1.8498218181818187 | 5.370544462809917 | 3.4079715702479345 | 3.8684508181818176 | 1.0066035932859554 | 15.633756547520665 | 3.4218407590214897 | 36.820366186531174 | 15.250190459474835 | 18.943982616096033 | 6.067978756268942 | 3.9051492237140994 | 4.352468565779199 |
| user_002 | Jogging | 1.446034907403e12 | -16.32385 | -2.98839 | -6.70665 | -18.60913818181818 | -8.074537272727271 | -9.162636363636363 | 266.4680788225 | 8.9304747921 | 44.9791542225 | 17.899097961548232 | 22.465247190479637 | 2.2852881818181814 | 5.086147272727271 | 2.455986363636363 | 2.8151174380165287 | 3.4934809917355367 | 3.2850951074380172 | 5.222542073957849 | 25.868894079871062 | 6.0318690183677655 | 13.08808219941119 | 15.12350955082885 | 15.124677542114034 | 3.6177454580734656 | 3.888895672402237 | 3.8890458395490834 |
| user_002 | Jogging | 1.44603490763e12 | -7.77842 | -0.804128 | 2.33694 | -1.309259 | -1.4346706363636363 | -0.27232436363636364 | 60.50381769639999 | 0.646621840384 | 5.461288563599999 | 8.161600829517699 | 4.856999590605138 | 6.469161 | 0.6305426363636364 | 2.6092643636363633 | 3.561889454545455 | 0.643527520661157 | 1.4802419504132232 | 41.850044043920995 | 0.39758401627240497 | 6.808260519342676 | 20.831291931262594 | 0.5178461229852592 | 3.4899073511287853 | 4.564131016005412 | 0.719615260389369 | 1.8681293721605003 |
| user_002 | Jogging | 1.446034907654e12 | -8.31491 | -7.62514 | -6.47673 | -4.3923026363636355 | -2.256814909090909 | -1.2094304545454546 | 69.1377283081 | 58.1427600196 | 41.9480314929 | 13.008786254704933 | 6.817305834917863 | 3.922607363636364 | 5.3683250909090905 | 5.267299545454545 | 4.743168247933884 | 1.1372572975206612 | 2.2691339586776857 | 15.386848529254225 | 28.818914281684094 | 27.744444501545658 | 27.438662371744726 | 3.279949445254316 | 7.294591790096374 | 5.23819266271724 | 1.8110630704794122 | 2.7008501976408046 |
| user_002 | Jogging | 1.446034907678e12 | -14.82936 | -13.948 | 3.63983 | -8.067568181818181 | -5.447853090909091 | -0.7112665454545454 | 219.90991800959998 | 194.546704 | 13.248362428899998 | 20.681029578783065 | 10.8121546065273 | 6.761791818181818 | 8.500146909090908 | 4.351096545454546 | 5.643219752066115 | 3.4941161239669416 | 2.729611066115702 | 45.721828592430576 | 72.25249747612773 | 18.93204114786648 | 33.4239033673911 | 27.913006124494846 | 9.746806300428416 | 5.781340966193838 | 5.283276078769199 | 3.1219875560976242 |
| user_002 | Jogging | 1.446034907904e12 | -4.3296 | 2.75966 | -1.30349 | -7.5589545454545455 | -2.8560068181818186 | -2.797982727272727 | 18.74543616 | 7.615723315599999 | 1.6990861801000001 | 5.297192242660256 | 8.883504805448565 | 3.2293545454545454 | 5.615666818181818 | 1.494492727272727 | 4.654491239669421 | 4.516412933884298 | 1.1739957190082644 | 10.428730780247934 | 31.53571381282831 | 2.233508511871074 | 23.339964729773104 | 20.7930003177753 | 2.0841700125165734 | 4.831145281377192 | 4.559934244896005 | 1.4436654780511216 |
| user_002 | Jogging | 1.446034907913e12 | -3.63983 | 4.56072 | -1.34181 | -6.733325454545454 | -1.8318086363636361 | -2.6168318181818178 | 13.248362428899998 | 20.8001669184 | 1.8004540760999999 | 5.987402059608157 | 8.085788948485714 | 3.0934954545454545 | 6.392528636363636 | 1.2750218181818178 | 4.686161570247934 | 4.656393636363637 | 1.0932376942148758 | 9.569714127293388 | 40.86442236672913 | 1.6256806368396686 | 23.52487566892179 | 22.36712104831754 | 1.8064930810530717 | 4.850244908138329 | 4.729389077705231 | 1.3440584366213664 |
| user_002 | Jogging | 1.446034907936e12 | -3.06503 | 3.18118 | -3.33447 | -4.468944545454546 | 0.7042995454545454 | -2.491420909090909 | 9.3944089009 | 10.1199061924 | 11.1186901809 | 5.534709140885363 | 6.1632089767866995 | 1.4039145454545459 | 2.4768804545454546 | 0.8430490909090911 | 4.319109421487604 | 4.469225578512397 | 0.7322038429752066 | 1.970976050938844 | 6.134936786109297 | 0.710731769682645 | 21.374971039417286 | 21.089082884775454 | 0.7007315357295453 | 4.6233073702077485 | 4.592285148460999 | 0.8370970885922047 |
| user_002 | Jogging | 1.446034908074e12 | -9.69444 | -11.8787 | -0.805325 | -4.068322727272728 | 0.3489659090909093 | -2.3416238181818185 | 93.9821669136 | 141.10351369 | 0.6485483556249999 | 15.353638948445576 | 6.256494405692689 | 5.626117272727273 | 12.22766590909091 | 1.5362988181818187 | 3.350651404958678 | 4.778638016528926 | 0.9405909669421487 | 31.653195566480164 | 149.51581358434402 | 2.3602140587468527 | 14.281345570168895 | 30.914827648535017 | 1.0798813779074146 | 3.779066759157464 | 5.560110398952077 | 1.0391734108932034 |
| user_002 | Jogging | 1.446034908139e12 | -16.20889 | -13.83303 | -8.43107 | -5.102970909090908 | -0.7588395454545455 | -2.811918363636364 | 262.7281150321 | 191.35271898090002 | 71.08294134490001 | 22.91645206741 | 7.7895724282934165 | 11.105919090909092 | 13.074190454545455 | 5.619151636363636 | 3.8196790082644627 | 5.613133347107438 | 1.4023345041322315 | 123.34143885381903 | 170.9344560417275 | 31.57486511244813 | 22.279454978312547 | 45.07532239502894 | 3.9238172475664856 | 4.720111754854174 | 6.713815785008473 | 1.9808627533391823 |
| user_002 | Jogging | 1.446034908463e12 | -9.38788 | -8.92803 | -0.920286 | -5.604617272727274 | -3.486549818181818 | -2.428715181818182 | 88.13229089439999 | 79.7097196809 | 0.8469263217960001 | 12.988030524182486 | 9.187988234579967 | 3.7832627272727253 | 5.441480181818182 | 1.5084291818181819 | 4.455290578512398 | 4.438821066115702 | 2.185842553719008 | 14.313076863571059 | 29.609706569120036 | 2.2753585965606695 | 32.435867004961985 | 37.61418130025047 | 6.970272935876789 | 5.695249512090053 | 6.1330401352225365 | 2.6401274469003932 |
| user_002 | Jogging | 1.446034908916e12 | -16.43882 | -15.02096 | -10.73029 | -7.273292727272727 | -6.155037090909091 | -1.672757909090909 | 270.2348029924 | 225.6292393216 | 115.1391234841 | 24.718478225774742 | 11.284802730750444 | 9.165527272727273 | 8.865922909090909 | 9.057532090909092 | 5.137139504132232 | 4.605721454545455 | 2.7612792479338846 | 84.00689018710744 | 78.604589029943 | 82.03888757784803 | 38.23730245817776 | 30.93058873133441 | 12.517200853787648 | 6.183631817805598 | 5.561527553769235 | 3.537965637734155 |
| user_002 | Jogging | 1.446034908981e12 | -14.5228 | -4.86608 | -7.05154 | -8.25917 | -6.4372134545454545 | -2.449615181818182 | 210.91171984000002 | 23.678734566400003 | 49.7242163716 | 16.861633099376824 | 12.42281859930853 | 6.263630000000001 | 1.5711334545454543 | 4.601924818181818 | 5.498807190082645 | 4.714031438016529 | 2.795482975206612 | 39.23306077690001 | 2.468460331991933 | 21.177712032197757 | 41.32916892800894 | 31.141886030605203 | 12.8191379720562 | 6.4287766276336695 | 5.5804915581519525 | 3.58038237791108 |
| user_002 | Jogging | 1.446034911117e12 | -4.138 | 1.57173 | -2.87462 | -8.47515909090909 | -6.597462727272728 | -2.5053534545454546 | 17.123044 | 2.4703351929000004 | 8.2634401444 | 5.277955980993021 | 12.378208799153429 | 4.33715909090909 | 8.169192727272728 | 0.3692665454545456 | 5.41583082644628 | 5.39176394214876 | 2.1766558512396696 | 18.81094897985536 | 66.73570981532563 | 0.136357781591934 | 44.27695044952525 | 35.340990234223725 | 8.265993487591441 | 6.654092759311765 | 5.9448288650072785 | 2.8750640840842907 |
| user_002 | Jogging | 1.446034911505e12 | -0.880768 | -7.6042e-2 | 0.459245 | -9.063898 | -6.924927454545455 | -2.4043275454545454 | 0.775752269824 | 5.782385764e-3 | 0.210905970025 | 0.9962131426622518 | 12.229235132780667 | 8.18313 | 6.848885454545455 | 2.8635725454545455 | 4.822659256198348 | 5.042447603305785 | 2.4689677851239673 | 66.96361659690001 | 46.9072319694843 | 8.200047723081026 | 32.30563775185838 | 33.39696622379565 | 9.388849709188909 | 5.683804865744282 | 5.779010834372579 | 3.0641229918508346 |
| user_002 | Jogging | 1.446034912671e12 | -6.13065 | -0.420925 | -3.10454 | -8.461222636363637 | -6.231678 | -2.132602181818182 | 37.5848694225 | 0.177177855625 | 9.638168611600001 | 6.884781470005057 | 12.102373588962756 | 2.3305726363636365 | 5.810753 | 0.9719378181818179 | 5.907028347107438 | 4.40873579338843 | 2.5066566363636364 | 5.431568813366951 | 33.764850427009 | 0.9446631224120325 | 50.828857630514584 | 25.11324930487152 | 9.586059643286172 | 7.129435996663031 | 5.011312134049477 | 3.096136244302917 |
| user_002 | Jogging | 1.446034913448e12 | -7.12698 | -0.727487 | -2.2615 | -8.994222636363636 | -6.663652454545454 | -2.1604711818181817 | 50.79384392039999 | 0.529237335169 | 5.114382249999999 | 7.5124871717407276 | 12.297534620940462 | 1.8672426363636365 | 5.936165454545454 | 0.10102881818181819 | 5.128271347107439 | 4.81854147107438 | 2.129470016528926 | 3.4865950630542235 | 35.238060303738834 | 1.0206822103214878e-2 | 38.6690300335663 | 30.39342674095808 | 6.766964182928945 | 6.218442733801309 | 5.513023375694872 | 2.601338921195957 |
| user_002 | Jogging | 1.446034915132e12 | -3.79311 | -0.650847 | 0.267643 | -8.910616363636363 | -4.698865454545454 | -0.1364618181818182 | 14.387683472099999 | 0.42360181740899994 | 7.163277544900001e-2 | 3.8578385224057783 | 10.584969232379587 | 5.1175063636363625 | 4.0480184545454545 | 0.4041048181818182 | 4.847044462809918 | 4.361232578512396 | 1.7323318264462806 | 26.188871381858664 | 16.38645340834057 | 0.16330070407776034 | 29.31876509625081 | 22.243799336105926 | 4.342268189926172 | 5.4146805165448875 | 4.716333251171499 | 2.083810977494401 |
| user_002 | Jogging | 1.446034915197e12 | -9.57948 | -6.93538 | 7.6042e-2 | -8.52393 | -4.493329090909091 | 0.31293109090909094 | 91.7664370704 | 48.0994957444 | 5.782385764e-3 | 11.826737301579165 | 10.086547002310532 | 1.0555500000000002 | 2.4420509090909093 | 0.23688909090909094 | 4.456240859504132 | 4.307077289256198 | 1.427036123966942 | 1.1141858025000004 | 5.963612642591737 | 5.611644139173555e-2 | 26.813737589387326 | 21.947038640486916 | 3.1723656216127227 | 5.178198295680393 | 4.684766658061735 | 1.781113590317227 |
| user_002 | Jumping | 1.446035035287e12 | -15.55745 | -19.38948 | 1.26397 | -6.910992181818182 | -8.753850818181819 | 0.2850607272727273 | 242.0342505025 | 375.95193467039996 | 1.5976201609 | 24.89144040295378 | 11.624306276171978 | 8.646457818181817 | 10.63562918181818 | 0.9789092727272728 | 4.716565537190082 | 6.76115967768595 | 0.7705225371900827 | 74.76123280159747 | 113.11660809314245 | 0.9582633642314381 | 27.958317956097122 | 53.20256051849058 | 0.6330625595010736 | 5.287562572310338 | 7.2940085356743705 | 0.7956522855500847 |
| user_002 | Jumping | 1.446035035351e12 | -14.75272 | -19.46612 | 0.995729 | -7.381286727272727 | -9.38787809090909 | 0.29551172727272723 | 217.6427473984 | 378.9298278544 | 0.991476241441 | 24.445123266088086 | 12.413256938175369 | 7.371433272727273 | 10.07824190909091 | 0.7002172727272727 | 5.027878347107438 | 7.162414743801653 | 0.787307570247934 | 54.338028494270716 | 101.57095997815638 | 0.4903042290256199 | 31.481888844794117 | 59.51938985669653 | 0.6534697889596003 | 5.610872378230868 | 7.714881065622238 | 0.8083747824861933 |
| user_002 | Jumping | 1.44603503561e12 | -0.420925 | -1.30229 | -0.307161 | -4.848662272727272 | -6.008723000000001 | -8.420718181818178e-2 | 0.177177855625 | 1.6959592440999998 | 9.434787992100001e-2 | 1.402670659722374 | 8.259123344540706 | 4.427737272727272 | 4.7064330000000005 | 0.22295381818181825 | 4.2412035206611565 | 6.341853776859505 | 0.7100340247933885 | 19.604857356298343 | 22.150511583489006 | 4.970840504185127e-2 | 22.47600091925477 | 45.354138884615956 | 0.573384619427679 | 4.740886090094843 | 6.734548157420508 | 0.7572216448489035 |
| user_002 | Jumping | 1.446035036388e12 | -5.63249 | -10.38421 | 0.229323 | -5.413016636363636 | -6.750744090909091 | -0.36638345454545457 | 31.724943600099998 | 107.83181732409999 | 5.2589038329e-2 | 11.815640057251615 | 9.290682276237781 | 0.2194733636363635 | 3.6334659090909085 | 0.5957064545454546 | 4.421087884297521 | 5.95073317355372 | 0.7208016280991736 | 4.816855734585944e-2 | 13.202074512525822 | 0.3548661799871157 | 26.6963255865405 | 41.226568967934206 | 0.7234207346930931 | 5.166848709468906 | 6.420791926852497 | 0.8505414361999615 |
| user_002 | Jumping | 1.446035036453e12 | -12.6451 | -17.97163 | 1.26397 | -6.409344818181818 | -8.192981363636365 | -7.375618181818186e-2 | 159.89855400999997 | 322.97948485690006 | 1.5976201609 | 22.010807777721382 | 10.988756568301302 | 6.235755181818181 | 9.778648636363636 | 1.3377261818181818 | 4.689329272727273 | 6.469166107438017 | 0.6843818347107438 | 38.884642687572295 | 95.6219691534564 | 1.7895113375218512 | 29.250212975164427 | 48.40921619558689 | 0.6113908975338558 | 5.40834660272106 | 6.9576731883286165 | 0.7819148914900239 |
| user_002 | Jumping | 1.44603503736e12 | 0.268841 | -0.995729 | -0.958607 | -4.831242636363636 | -5.886794363636364 | -0.4221228181818182 | 7.2275483281e-2 | 0.991476241441 | 0.918927380449 | 1.40807638470752 | 8.342801136380048 | 5.100083636363636 | 4.891065363636364 | 0.5364841818181818 | 4.486643495867768 | 6.314617280991735 | 0.684381867768595 | 26.01085309790413 | 23.922520391363317 | 0.28781527734112394 | 24.721975480839447 | 45.716738325488144 | 0.7141433950516395 | 4.972119817627029 | 6.761415408439873 | 0.845070053339745 |
| user_002 | Jumping | 1.446035037424e12 | -1.49389 | 3.8919e-2 | -0.460442 | -4.886981 | -6.008722636363634 | -0.4116718181818182 | 2.2317073320999996 | 1.514688561e-3 | 0.21200683536400003 | 1.5637227554860866 | 8.32719446672629 | 3.3930909999999996 | 6.047641636363634 | 4.8770181818181824e-2 | 4.467958479338843 | 6.213274247933884 | 0.6476450247933885 | 11.513066534280998 | 36.573969361879016 | 2.378530634578513e-3 | 24.5913351297316 | 44.37799111909305 | 0.6957145180793509 | 4.958965126892061 | 6.661680802852464 | 0.8340950294057332 |
| user_002 | Jumping | 1.446035037683e12 | -16.89866 | -19.58108 | -0.690364 | -7.335999 | -9.234596181818183 | -0.2897431818181818 | 285.5647097956 | 383.4186939664 | 0.476602452496 | 25.873925218537984 | 12.289344042912308 | 9.562660999999999 | 10.346483818181817 | 0.40062081818181816 | 5.574496611570248 | 6.9480109586776875 | 0.49309669421487606 | 91.44448540092097 | 107.04972739989819 | 0.1604970399606694 | 38.60496422999283 | 57.953553301972086 | 0.3831870005899715 | 6.213289324503796 | 7.61272312001245 | 0.6190210017357825 |
| user_002 | Jumping | 1.446035037813e12 | -1.68549 | -1.83878 | 0.382604 | -5.865892636363636 | -7.395221636363636 | -0.36638363636363636 | 2.8408765400999996 | 3.3811118884000004 | 0.146385820816 | 2.5235637993353763 | 9.925484171249254 | 4.180402636363636 | 5.556441636363636 | 0.7489876363636363 | 4.904050214876033 | 6.237026396694215 | 0.4500260495867768 | 17.47576620211604 | 30.8740436583154 | 0.5609824794255867 | 29.490202021043206 | 46.183238745060336 | 0.2788734853397626 | 5.430488193619723 | 6.795825096709033 | 0.5280847331061206 |
| user_002 | Jumping | 1.446035040208e12 | -0.459245 | 0.920286 | -1.34181 | -5.315474000000001 | -6.677586363636363 | -0.6137239999999999 | 0.210905970025 | 0.8469263217960001 | 1.8004540760999999 | 1.6906467306687698 | 9.261843463669647 | 4.856229000000001 | 7.5978723636363625 | 0.728086 | 4.325128140495868 | 5.7214437024793385 | 0.6352940247933886 | 23.582960100441007 | 57.72766445410921 | 0.530109223396 | 23.31046909148014 | 42.84138752310991 | 0.4917808251254432 | 4.828091661462129 | 6.545333262952308 | 0.7012708643066837 |
| user_002 | Jumping | 1.446035040531e12 | -9.65612 | -12.60678 | -1.15021 | -6.005239272727272 | -7.621659636363637 | -0.746102909090909 | 93.24065345439999 | 158.9309019684 | 1.3229830441 | 15.92151181473983 | 10.210010521380092 | 3.6508807272727273 | 4.985120363636364 | 0.4041070909090909 | 4.329245619834711 | 5.909877884297521 | 0.5887393388429752 | 13.328930084771438 | 24.851425039941955 | 0.16330254092300828 | 22.52924776787061 | 44.68324583485345 | 0.4008151098503697 | 4.746498474440987 | 6.684552777475353 | 0.633099604999379 |
| user_002 | Jumping | 1.446035041049e12 | -2.72014 | -3.37159 | -1.76333 | -5.3955969999999995 | -6.377990545454546 | -0.6938477272727273 | 7.399161619599999 | 11.3676191281 | 3.1093326889000004 | 4.677190763332194 | 8.884110392773467 | 2.6754569999999998 | 3.006400545454546 | 1.0694822727272728 | 4.5788023388429755 | 5.791433371900829 | 0.5086150413223138 | 7.158070158848998 | 9.038444239709392 | 1.1437923316778926 | 26.28320640632546 | 37.88538753004604 | 0.3918992896498903 | 5.126714972214221 | 6.155110683817639 | 0.6260186016804056 |
| user_002 | Jumping | 1.446035042602e12 | 1.64837 | 1.53341 | -1.26517 | -4.392303181818181 | -6.851770272727273 | -0.7391359090909091 | 2.7171236568999997 | 2.3513462280999997 | 1.6006551288999997 | 2.5824649104876523 | 8.912902370235305 | 6.040673181818181 | 8.385180272727272 | 0.5260340909090908 | 3.7911784380165288 | 6.163553950413223 | 0.4873965289256199 | 36.48973248953739 | 70.31124820613461 | 0.27671186479855364 | 17.489989493492825 | 43.924118922545375 | 0.29041824755649287 | 4.182103477138368 | 6.627527361131403 | 0.5389046739048502 |
| user_002 | Jumping | 1.446035042668e12 | 0.422122 | -1.18733 | -0.728685 | -4.364433909090908 | -6.844803 | -0.7286849090909091 | 0.178186982884 | 1.4097525289 | 0.530981829225 | 1.4556515177091665 | 8.906684790307402 | 4.786555909090908 | 5.6574729999999995 | 9.0909090921798e-8 | 3.777243776859504 | 6.1201664876033055 | 0.4560435537190083 | 22.911117470853092 | 32.007000745728995 | 8.264462812227734e-15 | 17.35445550149097 | 43.41248493255424 | 0.2796051422585815 | 4.165867916952117 | 6.58881513874492 | 0.5287770250857932 |
| user_002 | Jumping | 1.446035043121e12 | -4.3296 | -6.74378 | 0.114362 | -5.043748636363636 | -7.980478090909091 | -0.9655744545454547 | 18.74543616 | 45.4785686884 | 1.3078667044000002e-2 | 8.014804022273033 | 10.159283198878056 | 0.7141486363636362 | 1.2366980909090906 | 1.0799364545454546 | 3.8741517190082644 | 6.2810481570247925 | 0.9665591404958679 | 0.5100082748200412 | 1.5294221680581892 | 1.1662627458562067 | 20.302119742441093 | 47.49064113773937 | 1.4988273611587017 | 4.50578736098821 | 6.891345379368195 | 1.2242660499902387 |
| user_002 | Jumping | 1.44603504338e12 | -1.95374 | -0.612526 | -1.34181 | -4.347015636363637 | -6.057495727272727 | -0.937705 | 3.8170999876000002 | 0.375188100676 | 1.8004540760999999 | 2.4480077949990275 | 8.273893755622877 | 2.3932756363636374 | 5.444969727272727 | 0.40410499999999994 | 3.9118382975206614 | 6.211057958677685 | 1.0739194297520658 | 5.727768271611773 | 29.647695330916434 | 0.16330085102499994 | 19.778612203884183 | 46.26945035536362 | 1.6057372723978836 | 4.447315168040622 | 6.80216512261821 | 1.2671768907291057 |
| user_002 | Jumping | 1.44603504448e12 | -0.459245 | -0.229323 | -1.18853 | -5.287603090909091 | -6.9771812727272735 | -0.9307369090909091 | 0.210905970025 | 5.2589038329e-2 | 1.4126035609000003 | 1.2946422553176613 | 9.476152949648709 | 4.828358090909091 | 6.747858272727274 | 0.25779309090909097 | 4.262104669421488 | 6.1702033719008265 | 0.6112244876033058 | 23.313041854047277 | 45.5335912688139 | 6.645727772046284e-2 | 23.640955114855853 | 45.134428060760406 | 0.4930197020222338 | 4.862196531903647 | 6.718216136800036 | 0.7021536171111232 |
| user_002 | Jumping | 1.446035044739e12 | -11.18893 | -16.89866 | -1.03525 | -6.3954082727272725 | -8.757333454545455 | -0.8854496363636364 | 125.19215454489998 | 285.5647097956 | 1.0717425625 | 20.293560725092085 | 11.359921231515365 | 4.793521727272727 | 8.141326545454545 | 0.14980036363636362 | 4.538898115702478 | 6.712388214876032 | 0.5801884545454545 | 22.977850549835704 | 66.28119791972283 | 2.2440148945586772e-2 | 25.736066941496144 | 50.24123463081261 | 0.45139800147421877 | 5.07307273173726 | 7.088105150942148 | 0.6718615939866028 |
| user_002 | Jumping | 1.446035046163e12 | 0.11556 | 1.41845 | -1.45677 | -4.918334727272727 | -6.743776545454546 | -0.8715144545454545 | 1.3354113599999998e-2 | 2.0120004025 | 2.1221788328999995 | 2.036549373081831 | 9.056364871324707 | 5.0338947272727275 | 8.162226545454546 | 0.5852555454545454 | 4.4670074462809914 | 6.255394834710743 | 0.7227016694214875 | 25.340096125264168 | 66.62194217932286 | 0.34252405348529746 | 22.30188578895255 | 43.665427425798704 | 0.8360514631476093 | 4.722487246033868 | 6.607982099385462 | 0.9143584981546402 |
| user_002 | Jumping | 1.446035046551e12 | -15.21256 | -19.58108 | -0.996927 | -7.221037636363636 | -9.311237181818182 | -0.9168020909090909 | 231.4219817536 | 383.4186939664 | 0.993863443329 | 24.816013764570027 | 12.226709343990398 | 7.9915223636363635 | 10.269842818181818 | 8.012490909090908e-2 | 4.73936579338843 | 6.616745652892562 | 0.7619723223140497 | 63.86442968850013 | 105.46967151016067 | 6.420001056826444e-3 | 27.295248065600617 | 51.45718724716073 | 0.87134305900294 | 5.224485435485549 | 7.1733665211782345 | 0.9334575828621995 |
| user_002 | Jumping | 1.44603504694e12 | -0.650847 | -0.535886 | 0.152682 | -5.015878818181817 | -6.398894181818182 | -0.606755909090909 | 0.42360181740899994 | 0.287173804996 | 2.3311793124000002e-2 | 0.8567890145940247 | 8.614114338184608 | 4.3650318181818175 | 5.8630081818181825 | 0.759437909090909 | 4.247853950413224 | 5.7736992644628105 | 0.5811379752066116 | 19.053502773739663 | 34.37486494006695 | 0.5767459377643718 | 22.57831495566934 | 40.71143977129847 | 0.4586385032268108 | 4.751664440558629 | 6.380551682362464 | 0.67722854578555 |
| user_002 | Jumping | 1.446035047134e12 | -15.28921 | -19.50444 | -1.18853 | -7.189685909090909 | -9.227629636363638 | -0.6276584545454545 | 233.75994242410002 | 380.42317971359995 | 1.4126035609000003 | 24.81120161738645 | 12.127786396150414 | 8.099524090909092 | 10.276810363636361 | 0.5608715454545455 | 5.05701467768595 | 6.479300024793388 | 0.5304669421487603 | 65.60229049921676 | 105.61283125014371 | 0.31457689050057036 | 30.71943408184397 | 50.9383244056931 | 0.4049408038286176 | 5.542511531954081 | 7.137108966920226 | 0.6363495924636218 |
| user_002 | Jumping | 1.446035047846e12 | -0.957409 | -3.63983 | -0.268841 | -6.489468363636363 | -8.189497000000001 | -0.8854503636363634 | 0.9166319932809999 | 13.248362428899998 | 7.2275483281e-2 | 3.773230698680111 | 10.932699234859088 | 5.532059363636363 | 4.549667000000001 | 0.6166093636363634 | 5.063348652892563 | 5.8664919669421485 | 0.6679141570247933 | 30.603680802796763 | 20.69946981088901 | 0.38020710732404106 | 32.91277448823795 | 42.085032088373026 | 0.6403729603527354 | 5.736965616790635 | 6.487297749323136 | 0.8002330662705306 |
| user_002 | Jumping | 1.44603504804e12 | -0.727487 | -0.191003 | -0.498763 | -4.897433727272727 | -6.269997272727273 | -0.9377052727272727 | 0.529237335169 | 3.6482146009e-2 | 0.24876453016900002 | 0.9024876793325214 | 8.618971209554099 | 4.169946727272727 | 6.0789942727272726 | 0.43894227272727265 | 5.221379917355372 | 6.031807355371901 | 0.6270602231404957 | 17.388455708292526 | 36.95417136785098 | 0.1926703187869834 | 34.153680448202515 | 42.71723581389732 | 0.6066162822620906 | 5.8441150269482645 | 6.535842395123778 | 0.7788557518963898 |
| user_002 | Jumping | 1.446035048363e12 | -14.29288 | -18.73803 | -1.41845 | -7.2732934545454535 | -9.192791454545455 | -1.1188553636363636 | 204.2864186944 | 351.11376828089993 | 2.0120004025 | 23.609578297330934 | 12.381614214269916 | 7.019586545454547 | 9.545238545454543 | 0.29959463636363637 | 5.931730933884297 | 6.700986851239668 | 0.8199282727272726 | 49.2745952691265 | 91.11157888963116 | 8.97569461378595e-2 | 40.95245127848688 | 52.09298729431737 | 0.9762669956791471 | 6.399410228957578 | 7.2175471799162745 | 0.9880622428162849 |
| user_002 | Jumping | 1.446035048622e12 | 0.613724 | -1.64717 | -0.575403 | -4.855630545454546 | -6.144584181818183 | -0.8819659999999999 | 0.37665714817600005 | 2.7131690089 | 0.331088612409 | 1.849571509697584 | 8.507275707386022 | 5.4693545454545465 | 4.497414181818183 | 0.3065629999999999 | 4.759319694214875 | 5.8956271322314056 | 0.7765407685950414 | 29.91383914388431 | 20.226734322819315 | 9.398087296899996e-2 | 27.058965387570314 | 42.589898152646015 | 0.8881102471901253 | 5.201823275311294 | 6.52609363652147 | 0.9423960139931223 |
| user_002 | Jumping | 1.446035048816e12 | -13.06663 | -17.89499 | 0.612526 | -6.47553490909091 | -8.617987 | -0.7356520909090908 | 170.7368195569 | 320.2306671001 | 0.375188100676 | 22.166250805169465 | 11.374319492274104 | 6.59109509090909 | 9.277003 | 1.3481780909090908 | 4.852428669421488 | 5.799985107438017 | 0.8816839008264463 | 43.44253449740591 | 86.06278466200901 | 1.8175841648072808 | 28.73750824814298 | 43.85939409204076 | 1.0641403804571345 | 5.360737658955434 | 6.622642530896618 | 1.031571800921843 |
| user_002 | Jumping | 1.446035049852e12 | -1.07237 | -1.34061 | -0.767005 | -5.040264909090909 | -6.653200636363636 | -0.3629003636363637 | 1.1499774169 | 1.7972351721000002 | 0.5882966700250001 | 1.88029499255436 | 8.79786931494273 | 3.9678949090909086 | 5.312590636363636 | 0.40410463636363636 | 4.169313190082645 | 6.042575611570248 | 0.658412867768595 | 15.74419000958955 | 28.22361926957858 | 0.16330055713058678 | 19.867216196014347 | 40.23047659035462 | 0.5792754519203643 | 4.457265551435582 | 6.3427499233656235 | 0.7611014728144758 |
| user_002 | Jumping | 1.446035050176e12 | -3.10335 | -5.55585 | 0.535886 | -5.6081021818181815 | -8.13027490909091 | -0.13994581818181817 | 9.6307812225 | 30.867469222500006 | 0.287173804996 | 6.38634670605942 | 10.273776829050883 | 2.5047521818181817 | 2.574424909090909 | 0.6758318181818181 | 4.204466925619835 | 5.875676900826446 | 0.6029905867768596 | 6.273783492322941 | 6.627663612547734 | 0.4567486464669421 | 22.885017149386655 | 40.31617521487341 | 0.5543265915647807 | 4.783828712379516 | 6.3495019658925544 | 0.7445311219584986 |
| user_002 | Jumping | 1.446035050241e12 | 0.498763 | 1.53341 | -0.843646 | -4.667512818181818 | -6.604429454545454 | -0.32458000000000004 | 0.24876453016900002 | 2.3513462280999997 | 0.711738573316 | 1.81984871118041 | 8.785329171203369 | 5.166275818181818 | 8.137839454545453 | 0.519066 | 4.421404578512397 | 6.080579181818181 | 0.5314171652892562 | 26.690405829530214 | 66.22443098795664 | 0.26942951235600004 | 24.608855252102714 | 43.18926455572344 | 0.42367363107114875 | 4.960731322305484 | 6.571853966402741 | 0.6509021670505858 |
| user_002 | Jumping | 1.446035050435e12 | -1.76214 | -1.37893 | -0.498763 | -4.650094727272728 | -6.531272545454545 | -0.303678 | 3.1051373796000004 | 1.9014479449 | 0.24876453016900002 | 2.292454984218665 | 8.549858401319002 | 2.8879547272727284 | 5.152342545454545 | 0.195085 | 4.284591338842975 | 5.999821479338842 | 0.4937302231404958 | 8.3402825067769 | 26.546633705701016 | 3.8058157225e-2 | 23.265654469793503 | 41.78307817709959 | 0.37553256642510663 | 4.8234484002416265 | 6.463983151053195 | 0.61280712008356 |
| user_002 | Sitting | 1.446034565105e12 | -0.842448 | -0.114362 | 9.4262 | -0.8169011666666667 | -0.13990883333333332 | 9.432586666666667 | 0.7097186327039999 | 1.3078667044000002e-2 | 88.85324643999999 | 9.46446214740954 | 9.46900969429797 | 2.5546833333333296e-2 | 2.554683333333331e-2 | 6.386666666667651e-3 | 1.5754019444444416e-2 | 1.9905258333333332e-2 | 1.788266666666723e-2 | 6.526406933611092e-4 | 6.5264069336111e-4 | 4.0789511111123685e-5 | 5.1015066870412e-4 | 5.621592646068979e-4 | 4.965443152592826e-4 | 2.2586515196110265e-2 | 2.370989803029313e-2 | 2.2283274338823784e-2 |
| user_002 | Sitting | 1.446034566399e12 | -1.03405 | -0.267643 | 9.4262 | -0.7867094545454545 | -0.1701008181818182 | 9.454069090909089 | 1.0692594024999997 | 7.163277544900001e-2 | 88.85324643999999 | 9.486524053516598 | 9.489156748152983 | 0.24734054545454542 | 9.754218181818183e-2 | 2.7869090909089067e-2 | 9.025852892561986e-2 | 4.4337586776859506e-2 | 3.0719338842975476e-2 | 6.117734542575205e-2 | 9.514477233851243e-3 | 7.766862280990708e-4 | 1.4356791376253942e-2 | 2.7250634901893316e-3 | 1.1639260946656965e-3 | 0.11981982881081887 | 5.2202140666732545e-2 | 3.4116361099415286e-2 |
| user_002 | Sitting | 1.446034566788e12 | -0.804128 | -0.191003 | 9.46452 | -0.7518728181818181 | -0.18403545454545453 | 9.447101818181817 | 0.646621840384 | 3.6482146009e-2 | 89.5771388304 | 9.500539080325547 | 9.479688318952602 | 5.2255181818181895e-2 | 6.967545454545476e-3 | 1.7418181818182887e-2 | 8.519149586776859e-2 | 5.9222429752066125e-2 | 3.610314049586772e-2 | 2.7306040268512475e-3 | 4.8546689661157324e-5 | 3.0339305785127694e-4 | 1.3260152495296018e-2 | 5.334299526162284e-3 | 1.8755207212622213e-3 | 0.11515273550939213 | 7.30362891045423e-2 | 4.330728254303451e-2 |
| user_002 | Sitting | 1.446034567695e12 | -0.765807 | -0.152682 | 9.46452 | -0.7762582727272725 | -0.14919863636363637 | 9.440134545454544 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.473291927813678 | 1.0451272727272531e-2 | 3.4833636363636455e-3 | 2.4385454545456042e-2 | 2.0585528925619823e-2 | 3.4836776859504134e-2 | 2.0901818181818334e-2 | 1.0922910161983061e-4 | 1.213382222314056e-5 | 5.946503933885027e-4 | 5.659854213441012e-4 | 1.7696435115822694e-3 | 5.758951861758233e-4 | 2.379044811146064e-2 | 4.20671310120178e-2 | 2.39978162793164e-2 |
| user_002 | Sitting | 1.446034569184e12 | -0.727487 | -7.6042e-2 | 9.4262 | -0.7449051818181818 | -0.13178036363636364 | 9.422716363636361 | 0.529237335169 | 5.782385764e-3 | 88.85324643999999 | 9.45453680308734 | 9.453159226117394 | 1.7418181818181777e-2 | 5.573836363636364e-2 | 3.483636363638354e-3 | 2.1535190082644606e-2 | 3.547014876033058e-2 | 3.420297520661222e-2 | 3.0339305785123823e-4 | 3.1067651808595045e-3 | 1.2135722314063454e-5 | 6.707755637640855e-4 | 2.3234757058317058e-3 | 1.8027063873779414e-3 | 2.5899335199268833e-2 | 4.820244501922808e-2 | 4.245828997237102e-2 |
| user_002 | Sitting | 1.446034569571e12 | -0.765807 | -0.114362 | 9.46452 | -0.7483889090909092 | -0.1422311818181818 | 9.429683636363636 | 0.586460361249 | 1.3078667044000002e-2 | 89.5771388304 | 9.496140155805042 | 9.460479984966504 | 1.7418090909090855e-2 | 2.7869181818181807e-2 | 3.4836363636364e-2 | 2.026850413223138e-2 | 2.0268504132231413e-2 | 1.8051570247934832e-2 | 3.033898909173535e-4 | 7.766912952148754e-4 | 1.213572231404984e-3 | 5.847311275935365e-4 | 7.08293521345605e-4 | 6.299543128475302e-4 | 2.418121435316135e-2 | 2.661378442359532e-2 | 2.5098890669659688e-2 |
| user_002 | Sitting | 1.446034570154e12 | -0.727487 | -0.152682 | 9.46452 | -0.7518725454545454 | -0.14571481818181817 | 9.450585454545456 | 0.529237335169 | 2.3311793124000002e-2 | 89.5771388304 | 9.493665675527708 | 9.48163678013592 | 2.438554545454541e-2 | 6.9671818181818446e-3 | 1.3934545454544534e-2 | 2.1535272727272715e-2 | 1.836833057851241e-2 | 2.691900826446285e-2 | 5.946548271157003e-4 | 4.8541622487603674e-5 | 1.941715570247677e-4 | 5.979690609361374e-4 | 7.038792646326072e-4 | 1.302935277535681e-3 | 2.4453405916888906e-2 | 2.6530723032601414e-2 | 3.609619477916863e-2 |
| user_002 | Sitting | 1.446034570673e12 | -0.765807 | -0.229323 | 9.38788 | -0.7623234545454545 | -0.16313336363636363 | 9.457552727272725 | 0.586460361249 | 5.2589038329e-2 | 88.13229089439999 | 9.421854397833687 | 9.489764290796622 | 3.4835454545455447e-3 | 6.618963636363637e-2 | 6.967272727272622e-2 | 1.4567942148760348e-2 | 3.768703305785125e-2 | 2.7552396694215112e-2 | 1.2135088933884925e-5 | 4.381067961950414e-3 | 4.854288925619688e-3 | 3.6407800337866306e-4 | 1.973747693268971e-3 | 1.5257912691209207e-3 | 1.9080828162809472e-2 | 4.4426880300882834e-2 | 3.9061378228640635e-2 |
| user_002 | Sitting | 1.446034570931e12 | -0.765807 | -0.152682 | 9.38788 | -0.7658071818181817 | -0.16661690909090907 | 9.433167272727273 | 0.586460361249 | 2.3311793124000002e-2 | 88.13229089439999 | 9.420300581657306 | 9.465767541734065 | 1.818181817325737e-7 | 1.3934909090909053e-2 | 4.528727272727373e-2 | 1.6468231404958688e-2 | 3.1036512396694218e-2 | 2.7552396694215275e-2 | 3.3057851208539194e-14 | 1.9418169137189978e-4 | 2.050937071074471e-3 | 5.406048559128475e-4 | 1.4474946131495118e-3 | 1.1727520745304068e-3 | 2.3250910862003826e-2 | 3.8045953965560014e-2 | 3.42454679414723e-2 |
| user_002 | Sitting | 1.446034571707e12 | -0.765807 | -0.114362 | 9.46452 | -0.7588398181818181 | -0.1701006363636364 | 9.450585454545454 | 0.586460361249 | 1.3078667044000002e-2 | 89.5771388304 | 9.496140155805042 | 9.482633327890168 | 6.9671818181819e-3 | 5.573863636363639e-2 | 1.393454545454631e-2 | 1.4568008264462806e-2 | 2.8819586776859505e-2 | 2.6602314049586958e-2 | 4.8541622487604446e-5 | 3.1067955836776894e-3 | 1.9417155702481723e-4 | 4.523384929909841e-4 | 1.4265323351600303e-3 | 1.0127811894816317e-3 | 2.1268250821141452e-2 | 3.776946299803626e-2 | 3.182422331309331e-2 |
| user_002 | Sitting | 1.44603457378e12 | -0.804128 | -0.114362 | 9.4262 | -0.748388909090909 | -0.13874763636363638 | 9.426199999999996 | 0.646621840384 | 1.3078667044000002e-2 | 88.85324643999999 | 9.461128206901542 | 9.4570153963809 | 5.5739090909090905e-2 | 2.4385636363636373e-2 | 3.552713678800501e-15 | 2.5018917355371924e-2 | 3.325310743801653e-2 | 2.121851239669487e-2 | 3.1068462553719e-3 | 5.946592608595046e-4 | 1.2621774483536189e-29 | 9.057764906649138e-4 | 1.7641233586235911e-3 | 7.160076165289551e-4 | 3.0096120857428018e-2 | 4.20014685293692e-2 | 2.6758318641666466e-2 |
| user_002 | Sitting | 1.446034574816e12 | -0.727487 | -0.191003 | 9.46452 | -0.7379379999999999 | -0.17706809090909092 | 9.44361818181818 | 0.529237335169 | 3.6482146009e-2 | 89.5771388304 | 9.494359289155746 | 9.474194624567115 | 1.0450999999999877e-2 | 1.3934909090909081e-2 | 2.0901818181819465e-2 | 2.6919115702479342e-2 | 3.261997520661157e-2 | 2.5968925619835336e-2 | 1.0922340099999743e-4 | 1.9418169137190054e-4 | 4.368860033058388e-4 | 1.0514057918069124e-3 | 1.4706621768903081e-3 | 1.0370526341097496e-3 | 3.242538807488528e-2 | 3.834921351071373e-2 | 3.220330160262686e-2 |
| user_002 | Sitting | 1.44603457514e12 | -0.765807 | -0.152682 | 9.38788 | -0.7483888181818181 | -0.17010072727272726 | 9.422716363636361 | 0.586460361249 | 2.3311793124000002e-2 | 88.13229089439999 | 9.420300581657306 | 9.454029645400169 | 1.7418181818181888e-2 | 1.7418727272727252e-2 | 3.483636363636222e-2 | 1.678478512396695e-2 | 3.4203421487603296e-2 | 2.216859504132206e-2 | 3.033930578512421e-4 | 3.034120598016522e-4 | 1.2135722314048601e-3 | 3.011860446724273e-4 | 1.4496971925725014e-3 | 7.171108640120033e-4 | 1.735471246297176e-2 | 3.807488926540038e-2 | 2.6778925744174342e-2 |
| user_002 | Sitting | 1.446034575204e12 | -0.765807 | -0.152682 | 9.4262 | -0.7483888181818181 | -0.17358436363636365 | 9.422716363636361 | 0.586460361249 | 2.3311793124000002e-2 | 88.85324643999999 | 9.458489234247349 | 9.454078824014742 | 1.7418181818181888e-2 | 2.0902363636363636e-2 | 3.483636363638354e-3 | 1.6784801652892575e-2 | 3.166988429752065e-2 | 2.1535206611570278e-2 | 3.033930578512421e-4 | 4.369088055867768e-4 | 1.2135722314063454e-5 | 3.0118662047708556e-4 | 1.2731764436769342e-3 | 7.082848841472434e-4 | 1.735472905225217e-2 | 3.568159810990722e-2 | 2.6613622153837748e-2 |
| user_002 | Sitting | 1.446034575723e12 | -0.727487 | -0.114362 | 9.38788 | -0.7553561818181816 | -0.15268218181818183 | 9.408781818181817 | 0.529237335169 | 1.3078667044000002e-2 | 88.13229089439999 | 9.4167195400847 | 9.440372344700105 | 2.7869181818181654e-2 | 3.832018181818182e-2 | 2.090181818181769e-2 | 2.18519834710744e-2 | 2.501922314049587e-2 | 1.4567933884297769e-2 | 7.766912952148669e-4 | 1.4684363345785127e-3 | 4.368860033057645e-4 | 5.880397760075145e-4 | 9.697739425169045e-4 | 3.5524568955670044e-4 | 2.424953145954607e-2 | 3.114119365915354e-2 | 1.8847962477591587e-2 |
| user_002 | Sitting | 1.446034576953e12 | -0.765807 | -0.114362 | 9.38788 | -0.7588399090909091 | -0.17010063636363637 | 9.412265454545452 | 0.586460361249 | 1.3078667044000002e-2 | 88.13229089439999 | 9.419757423771218 | 9.444454132222686 | 6.967090909090867e-3 | 5.5738636363636365e-2 | 2.438545454545249e-2 | 2.31188347107438e-2 | 3.166982644628099e-2 | 2.660231404958728e-2 | 4.854035573553661e-5 | 3.106795583677686e-3 | 5.946503933883295e-4 | 7.29263572803155e-4 | 1.7012463710616087e-3 | 9.421733505634972e-4 | 2.7004880536731782e-2 | 4.124616795608543e-2 | 3.069484240981695e-2 |
| user_002 | Sitting | 1.446034577536e12 | -0.804128 | -0.152682 | 9.4262 | -0.7588399090909091 | -0.15964963636363635 | 9.419232727272727 | 0.646621840384 | 2.3311793124000002e-2 | 88.85324643999999 | 9.461668989850997 | 9.45117952966258 | 4.5288090909090806e-2 | 6.9676363636363425e-3 | 6.967272727273155e-3 | 2.0901909090909096e-2 | 2.3752537190082638e-2 | 2.7552396694215275e-2 | 2.051011178190073e-3 | 4.8547956495867476e-5 | 4.854288925620431e-5 | 6.465170749398939e-4 | 7.954640647776109e-4 | 1.179371559429033e-3 | 2.542670004030987e-2 | 2.820397249994424e-2 | 3.434197955023899e-2 |
| user_002 | Sitting | 1.446034577665e12 | -0.689167 | -0.191003 | 9.38788 | -0.7518726363636362 | -0.15964963636363638 | 9.422716363636361 | 0.47495115388899994 | 3.6482146009e-2 | 88.13229089439999 | 9.415079616991987 | 9.454114323852655 | 6.270563636363624e-2 | 3.1353363636363624e-2 | 3.483636363636222e-2 | 2.6285743801652887e-2 | 2.311914049586777e-2 | 2.5968925619835336e-2 | 3.9319968317685795e-3 | 9.830334113140487e-4 | 1.2135722314048601e-3 | 1.0183138951352345e-3 | 6.961706396814426e-4 | 1.1473773824192664e-3 | 3.1911030931877374e-2 | 2.6385045758562604e-2 | 3.387295945764507e-2 |
| user_002 | Sitting | 1.446034577859e12 | -0.727487 | -0.191003 | 9.46452 | -0.7414216363636363 | -0.16661700000000002 | 9.4262 | 0.529237335169 | 3.6482146009e-2 | 89.5771388304 | 9.494359289155746 | 9.456861389481157 | 1.3934636363636344e-2 | 2.438599999999999e-2 | 3.8320000000000576e-2 | 2.4068859504132248e-2 | 1.9635495867768592e-2 | 2.1851900826447137e-2 | 1.941740905867763e-4 | 5.946769959999995e-4 | 1.4684224000000442e-3 | 8.627511631870764e-4 | 4.766212811788128e-4 | 9.98438972201379e-4 | 2.937262608598483e-2 | 2.1831657774406706e-2 | 3.159808494515734e-2 |
| user_002 | Sitting | 1.446034578442e12 | -0.765807 | -0.152682 | 9.46452 | -0.7483888181818181 | -0.15268236363636364 | 9.440134545454544 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.471073441510871 | 1.7418181818181888e-2 | 3.6363636363168084e-7 | 2.4385454545456042e-2 | 2.026844628099179e-2 | 2.7236148760330578e-2 | 2.1851900826446655e-2 | 3.033930578512421e-4 | 1.3223140495527202e-13 | 5.946503933885027e-4 | 4.523314104958706e-4 | 1.2158039469631855e-3 | 6.917361719008462e-4 | 2.1268084316549776e-2 | 3.486838033180184e-2 | 2.6300877778143567e-2 |
| user_002 | Sitting | 1.446034579154e12 | -0.804128 | -0.152682 | 9.46452 | -0.7518726363636364 | -0.15964963636363638 | 9.412265454545452 | 0.646621840384 | 2.3311793124000002e-2 | 89.5771388304 | 9.49984591790351 | 9.443711677109876 | 5.225536363636352e-2 | 6.96763636363637e-3 | 5.225454545454866e-2 | 2.7869239669421522e-2 | 2.438591735537191e-2 | 2.881917355371981e-2 | 2.7306230287685827e-3 | 4.854795649586786e-5 | 2.730537520661492e-3 | 1.3680450747032306e-3 | 8.440077025770098e-4 | 1.3801626013524098e-3 | 3.698709335299586e-2 | 2.905181065918284e-2 | 3.715053971818458e-2 |
| user_002 | Sitting | 1.446034579284e12 | -0.765807 | -0.152682 | 9.38788 | -0.7449052727272728 | -0.1526821818181818 | 9.412265454545455 | 0.586460361249 | 2.3311793124000002e-2 | 88.13229089439999 | 9.420300581657306 | 9.443018203023634 | 2.090172727272721e-2 | 1.8181818178808484e-7 | 2.4385454545456042e-2 | 2.6285694214876063e-2 | 1.83686694214876e-2 | 2.1218512396695356e-2 | 4.368822029834685e-4 | 3.3057851228725065e-14 | 5.946503933885027e-4 | 1.214685670752065e-3 | 6.244547164470323e-4 | 7.138011215627898e-4 | 3.4852340965164236e-2 | 2.4989091949229214e-2 | 2.6717056753369932e-2 |
| user_002 | Sitting | 1.446034579348e12 | -0.765807 | -0.191003 | 9.4262 | -0.7449052727272728 | -0.1526821818181818 | 9.415749090909088 | 0.586460361249 | 3.6482146009e-2 | 88.85324643999999 | 9.459185427258417 | 9.44648964216224 | 2.090172727272721e-2 | 3.8320818181818206e-2 | 1.0450909090911509e-2 | 2.7869165289256214e-2 | 1.900209090909091e-2 | 1.868495867768726e-2 | 4.368822029834685e-4 | 1.4684851061239688e-3 | 1.0922150082649681e-4 | 1.2532990447565726e-3 | 6.685877251743054e-4 | 5.902374034561036e-4 | 3.5401963854517625e-2 | 2.5857063351709245e-2 | 2.4294801984294987e-2 |
| user_002 | Sitting | 1.446034580061e12 | -0.765807 | -0.191003 | 9.50284 | -0.7518725454545454 | -0.17010063636363634 | 9.450585454545454 | 0.586460361249 | 3.6482146009e-2 | 90.30396806560002 | 9.535560317719039 | 9.482091909074576 | 1.3934454545454611e-2 | 2.0902363636363663e-2 | 5.2254545454546886e-2 | 1.9951809917355356e-2 | 3.1353165289256205e-2 | 3.008595041322418e-2 | 1.9416902347934065e-4 | 4.3690880558677796e-4 | 2.7305375206613065e-3 | 4.953656630586016e-4 | 1.7023433997783624e-3 | 1.3095547624343556e-3 | 2.22568116103498e-2 | 4.125946436611075e-2 | 3.6187770896179215e-2 |
| user_002 | Sitting | 1.44603458019e12 | -0.765807 | -7.6042e-2 | 9.4262 | -0.7518725454545454 | -0.15964963636363635 | 9.447101818181817 | 0.586460361249 | 5.782385764e-3 | 88.85324643999999 | 9.457562539418547 | 9.478472836423377 | 1.3934454545454611e-2 | 8.360763636363636e-2 | 2.090181818181769e-2 | 1.9951809917355366e-2 | 3.7053644628099174e-2 | 2.9769256198347967e-2 | 1.9416902347934065e-4 | 6.990236858314049e-3 | 4.368860033057645e-4 | 4.953658933816674e-4 | 2.245143004018783e-3 | 1.2290176961683736e-3 | 2.2256816784564395e-2 | 4.738294001029044e-2 | 3.505734867568244e-2 |
| user_002 | Sitting | 1.446034580838e12 | -0.765807 | -0.152682 | 9.46452 | -0.7553560909090908 | -0.1457149090909091 | 9.47497090909091 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.506230857534037 | 1.0450909090909177e-2 | 6.967090909090923e-3 | 1.0450909090909732e-2 | 1.80515454545455e-2 | 2.406889256198347e-2 | 2.248528925619892e-2 | 1.0922150082644809e-4 | 4.854035573553738e-5 | 1.092215008264597e-4 | 5.041842725206623e-4 | 1.2091784736033059e-3 | 9.785805175056312e-4 | 2.245404802080601e-2 | 3.477324364512614e-2 | 3.128227161677411e-2 |
| user_002 | Sitting | 1.446034581161e12 | -0.765807 | -0.191003 | 9.46452 | -0.7553561818181819 | -0.17358436363636365 | 9.471487272727273 | 0.586460361249 | 3.6482146009e-2 | 89.5771388304 | 9.497372338581762 | 9.503232204177522 | 1.0450818181818144e-2 | 1.741863636363636e-2 | 6.967272727273155e-3 | 2.2485388429752107e-2 | 2.660266942148761e-2 | 2.438545454545491e-2 | 1.0921960066942071e-4 | 3.0340889276859486e-4 | 4.854288925620431e-5 | 8.351673273809183e-4 | 1.0679732755770098e-3 | 1.1352416601051547e-3 | 2.8899261709962737e-2 | 3.267986039714689e-2 | 3.369334741614663e-2 |
| user_002 | Sitting | 1.446034581615e12 | -0.804128 | -0.191003 | 9.4262 | -0.7658072727272728 | -0.15268236363636364 | 9.440134545454542 | 0.646621840384 | 3.6482146009e-2 | 88.85324643999999 | 9.462364948911715 | 9.472572812816786 | 3.832072727272717e-2 | 3.832063636363636e-2 | 1.3934545454542757e-2 | 3.5469991735537214e-2 | 2.850295867768595e-2 | 2.470214876032967e-2 | 1.468478138710736e-3 | 1.4684711713140496e-3 | 1.9417155702471822e-4 | 2.1932858809804692e-3 | 1.4210125277220134e-3 | 8.340550972200801e-4 | 4.68325301577917e-2 | 3.7696319816687855e-2 | 2.8880012070982244e-2 |
| user_002 | Sitting | 1.44630938451e12 | -0.612526 | 3.25783 | 9.04299 | -0.7170360909090908 | 1.0840186363636362 | 9.300787272727275 | 0.375188100676 | 10.613456308899998 | 81.7756681401 | 9.631423184019898 | 9.533097720787474 | 0.1045100909090908 | 2.1738113636363634 | 0.2577972727272755 | 8.075772727272727e-2 | 0.963391652892562 | 0.11939528925619798 | 1.0922359101826424e-2 | 4.725455844674586 | 6.645943382562125e-2 | 9.939310662012773e-3 | 2.527474715107689 | 3.001030772569517e-2 | 9.969609150820695e-2 | 1.5898033573708696 | 0.1732348340423922 |
| user_002 | Sitting | 1.446309384769e12 | -0.650847 | 3.2195 | 9.11964 | -0.6438792727272727 | 2.3067839090909086 | 9.175375454545456 | 0.42360181740899994 | 10.36518025 | 83.1678337296 | 9.693122087181663 | 9.604403281195845 | 6.967727272727209e-3 | 0.9127160909090914 | 5.573545454545581e-2 | 9.374230578512394e-2 | 1.457437867768595 | 0.15961545454545503 | 4.854922334710655e-5 | 0.8330506626043729 | 3.106440893388571e-3 | 1.2473499554535682e-2 | 3.2644780912646967 | 3.788870107851274e-2 | 0.11168482240007226 | 1.8067866756384652 | 0.19465020184554843 |
| user_002 | Sitting | 1.446309385093e12 | -0.612526 | 3.25783 | 9.08132 | -0.5672386363636363 | 3.229953636363636 | 9.070865454545457 | 0.375188100676 | 10.613456308899998 | 82.47037294239999 | 9.667420408360028 | 9.645607770802847 | 4.5287363636363764e-2 | 2.78763636363637e-2 | 1.0454545454543052e-2 | 9.152531404958676e-2 | 1.008362305785124 | 0.12129611570247983 | 2.050945305132243e-3 | 7.770916495867804e-4 | 1.0929752066110678e-4 | 1.0810873121814417e-2 | 1.7490213705410085 | 2.3338032219158845e-2 | 0.1039753486255969 | 1.3225057166383094 | 0.15276790310519694 |
| user_002 | Sitting | 1.446309385158e12 | -0.574206 | 3.25783 | 9.11964 | -0.5707222727272727 | 3.2334381818181814 | 9.077833636363637 | 0.329712530436 | 10.613456308899998 | 83.1678337296 | 9.701082546238641 | 9.653528970031932 | 3.4837272727272772e-3 | 2.4391818181818348e-2 | 4.180636363636303e-2 | 7.410704132231402e-2 | 0.7888924132231406 | 9.817685950413252e-2 | 1.2136355710743833e-5 | 5.949607942148841e-4 | 1.747772040495817e-3 | 7.352153625508632e-3 | 1.2084774599566446 | 1.5525468077085118e-2 | 8.574470027651057e-2 | 1.0993077185013505 | 0.12460123625825355 |
| user_002 | Sitting | 1.446309385223e12 | -0.535886 | 3.2195 | 9.11964 | -0.563755 | 3.229953636363636 | 9.08480181818182 | 0.287173804996 | 10.36518025 | 83.1678337296 | 9.686082168998775 | 9.658497968666374 | 2.7869000000000033e-2 | 1.0453636363636054e-2 | 3.483818181818066e-2 | 6.713966942148758e-2 | 0.5922235289256196 | 7.790785123966935e-2 | 7.766811610000018e-4 | 1.0927851322313403e-4 | 1.2136989123966134e-3 | 6.429819267251685e-3 | 0.7789004993965207 | 9.594037630428333e-3 | 8.018615383750292e-2 | 0.8825533974760511 | 9.794915839571228e-2 |
| user_002 | Sitting | 1.446309386259e12 | -0.612526 | 3.2195 | 9.19628 | -0.5776896363636363 | 3.2229863636363634 | 9.157960000000001 | 0.375188100676 | 10.36518025 | 84.57156583839999 | 9.762783116974175 | 9.725842504903998 | 3.4836363636363665e-2 | 3.4863636363633432e-3 | 3.83199999999988e-2 | 2.2801991735537187e-2 | 2.8823636363636416e-2 | 2.7869090909089067e-2 | 1.2135722314049607e-3 | 1.2154731404956634e-5 | 1.468422399999908e-3 | 7.347628813275732e-4 | 1.1222698865514702e-3 | 9.9954221968436e-4 | 2.7106509943693843e-2 | 3.350029681288615e-2 | 3.1615537630797295e-2 |
| user_002 | Sitting | 1.446309386843e12 | -0.612526 | 3.18118 | 9.15796 | -0.5707223636363636 | 3.1985999999999994 | 9.157960000000001 | 0.375188100676 | 10.1199061924 | 83.86823136159998 | 9.714078734222612 | 9.717365950494086 | 4.1803636363636376e-2 | 1.7419999999999547e-2 | 1.7763568394002505e-15 | 2.4068760330578517e-2 | 2.94548760330576e-2 | 2.2801983471074812e-2 | 1.7475440132231415e-3 | 3.034563999999842e-4 | 3.1554436208840472e-30 | 8.517070569496617e-4 | 1.2942976006010338e-3 | 1.0723565535687397e-3 | 2.918402057547352e-2 | 3.597634779408596e-2 | 3.274685562872777e-2 |
| user_002 | Sitting | 1.446309387425e12 | -0.574206 | 3.25783 | 9.11964 | -0.595108 | 3.21602 | 9.16841090909091 | 0.329712530436 | 10.613456308899998 | 83.1678337296 | 9.701082546238641 | 9.734437069150347 | 2.0901999999999976e-2 | 4.18099999999999e-2 | 4.877090909091031e-2 | 2.818597520661156e-2 | 3.293892561983465e-2 | 2.6919008264462363e-2 | 4.36893603999999e-4 | 1.748076099999992e-3 | 2.378601573553838e-3 | 1.2720662864335061e-3 | 1.4499735449286213e-3 | 9.47689587978922e-4 | 3.566603827780016e-2 | 3.807851815562971e-2 | 3.0784567367090315e-2 |
| user_002 | Sitting | 1.446309388593e12 | -0.612526 | 3.18118 | 9.19628 | -0.6160097272727273 | 3.2090499999999995 | 9.182345454545455 | 0.375188100676 | 10.1199061924 | 84.57156583839999 | 9.750213337741693 | 9.74650685570224 | 3.4837272727272772e-3 | 2.7869999999999617e-2 | 1.3934545454544534e-2 | 6.01731404958678e-3 | 2.5338264462809675e-2 | 2.786909090909133e-2 | 1.2136355710743833e-5 | 7.767368999999787e-4 | 1.941715570247677e-4 | 1.2467220551239715e-4 | 1.0217695564988677e-3 | 1.0833890283997167e-3 | 1.1165670849187573e-2 | 3.196513032194406e-2 | 3.291487548813935e-2 |
| user_002 | Sitting | 1.446309389175e12 | -0.612526 | 3.2195 | 9.11964 | -0.6020752727272728 | 3.1811800000000003 | 9.168410909090909 | 0.375188100676 | 10.36518025 | 83.1678337296 | 9.690624442226413 | 9.723441354894987 | 1.0450727272727223e-2 | 3.831999999999969e-2 | 4.877090909090853e-2 | 2.6919157024793396e-2 | 3.420330578512377e-2 | 2.7235702479339062e-2 | 1.0921770052892458e-4 | 1.4684223999999761e-3 | 2.3786015735536644e-3 | 1.1308444474380178e-3 | 1.8865733498121553e-3 | 1.3283099696468977e-3 | 3.3628030680341925e-2 | 4.343470213794674e-2 | 3.644598701704891e-2 |
| user_002 | Sitting | 1.446309389241e12 | -0.574206 | 3.18118 | 9.15796 | -0.5985916363636363 | 3.1811800000000003 | 9.16841090909091 | 0.329712530436 | 10.1199061924 | 83.86823136159998 | 9.711737747923179 | 9.723228537958677 | 2.438563636363633e-2 | 4.440892098500626e-16 | 1.0450909090911509e-2 | 2.8819330578512402e-2 | 3.1036198347107306e-2 | 2.6285619834711064e-2 | 5.946592608595026e-4 | 1.9721522630525295e-31 | 1.0922150082649681e-4 | 1.183801075178814e-3 | 1.7762370850488212e-3 | 1.298522287603321e-3 | 3.4406410379154845e-2 | 4.214542780716339e-2 | 3.603501474404196e-2 |
| user_002 | Sitting | 1.446309389305e12 | -0.612526 | 3.2195 | 9.15796 | -0.5985916363636363 | 3.1846636363636365 | 9.164927272727274 | 0.375188100676 | 10.36518025 | 83.86823136159998 | 9.726695210207627 | 9.72109052636467 | 1.3934363636363689e-2 | 3.4836363636363554e-2 | 6.967272727274931e-3 | 2.9769388429752075e-2 | 3.16695041322313e-2 | 2.5652231404959282e-2 | 1.941664899504147e-4 | 1.213572231404953e-3 | 4.854288925622906e-5 | 1.2003492692006024e-3 | 1.815949387903819e-3 | 1.285283317806181e-3 | 3.464605705128078e-2 | 4.261395766534504e-2 | 3.585084821599317e-2 |
| user_002 | Sitting | 1.446309390147e12 | -0.612526 | 3.25783 | 9.15796 | -0.5916241818181818 | 3.222987272727273 | 9.171894545454547 | 0.375188100676 | 10.613456308899998 | 83.86823136159998 | 9.739449459347073 | 9.739772142778746 | 2.0901818181818244e-2 | 3.484272727272675e-2 | 1.3934545454548086e-2 | 2.216865289256201e-2 | 3.673975206611566e-2 | 2.0268429752067038e-2 | 4.368860033057877e-4 | 1.2140156438016163e-3 | 1.9417155702486672e-4 | 8.958447873020282e-4 | 1.9089935961682876e-3 | 5.251458019534503e-4 | 2.99306663357505e-2 | 4.369203126621933e-2 | 2.291605991337626e-2 |
| user_002 | Sitting | 1.446309391184e12 | -0.535886 | 3.2195 | 9.11964 | -0.5602713636363637 | 3.1846645454545457 | 9.14054181818182 | 0.287173804996 | 10.36518025 | 83.1678337296 | 9.686082168998775 | 9.695763475510269 | 2.4385363636363677e-2 | 3.4835454545454336e-2 | 2.0901818181819465e-2 | 2.9135975206611563e-2 | 2.3753719008264538e-2 | 1.7101487603305057e-2 | 5.94645959677688e-4 | 1.2135088933884152e-3 | 4.368860033058388e-4 | 1.1341499288587528e-3 | 1.1419832237415408e-3 | 4.0820156874531394e-4 | 3.367714252811175e-2 | 3.3793242279212286e-2 | 2.020399883056109e-2 |
| user_002 | Sitting | 1.446309391897e12 | -0.497565 | 3.14286 | 9.2346 | -0.5602712727272726 | 3.1846645454545452 | 9.154476363636364 | 0.24757092922499999 | 9.877568979600001 | 85.27783716 | 9.767444756374362 | 9.708977007952216 | 6.270627272727264e-2 | 4.180454545454504e-2 | 8.012363636363595e-2 | 3.451985123966941e-2 | 3.1353719008264426e-2 | 3.736991735537145e-2 | 3.932076639347097e-3 | 1.7476200206611222e-3 | 6.419797104132165e-3 | 1.5456712015822676e-3 | 1.2633272028549922e-3 | 1.8666947413974102e-3 | 3.931502513775449e-2 | 3.5543314460739196e-2 | 4.3205262890039335e-2 |
| user_002 | Sitting | 1.446309393711e12 | -0.535886 | 3.14286 | 9.15796 | -0.556787909090909 | 3.1742136363636364 | 9.137058181818183 | 0.287173804996 | 9.877568979600001 | 83.86823136159998 | 9.697060077476884 | 9.688865473566398 | 2.0901909090909054e-2 | 3.1353636363636195e-2 | 2.0901818181815912e-2 | 2.7552495867768565e-2 | 3.452090909090896e-2 | 2.8502479338842302e-2 | 4.368898036446266e-4 | 9.830505132231299e-4 | 4.3688600330569024e-4 | 1.119810475468068e-3 | 1.5546162080390586e-3 | 1.0547045938392187e-3 | 3.3463569377280546e-2 | 3.9428621685763485e-2 | 3.2476215817721414e-2 |
| user_002 | Sitting | 1.446309394034e12 | -0.535886 | 3.18118 | 9.15796 | -0.5637551818181818 | 3.1776972727272725 | 9.133574545454547 | 0.287173804996 | 10.1199061924 | 83.86823136159998 | 9.70954743327391 | 9.687111701292912 | 2.7869181818181876e-2 | 3.4827272727273595e-3 | 2.438545454545249e-2 | 2.3435454545454534e-2 | 3.1987190082644605e-2 | 1.9318347107437586e-2 | 7.766912952148793e-4 | 1.2129389256198951e-5 | 5.946503933883295e-4 | 9.510144742659655e-4 | 1.4509074897821133e-3 | 5.130100796393497e-4 | 3.0838522569441706e-2 | 3.809077959010702e-2 | 2.264972581819369e-2 |
| user_002 | Sitting | 1.446309394099e12 | -0.535886 | 3.2195 | 9.15796 | -0.5533041818181818 | 3.1742127272727276 | 9.133574545454548 | 0.287173804996 | 10.36518025 | 83.86823136159998 | 9.722169789537517 | 9.685314901567818 | 1.7418181818181777e-2 | 4.52872727272724e-2 | 2.4385454545450713e-2 | 1.773486776859502e-2 | 2.976958677685948e-2 | 1.9635041322313477e-2 | 3.0339305785123823e-4 | 2.0509370710743505e-3 | 5.946503933882428e-4 | 3.949631747610809e-4 | 1.1959536697220099e-3 | 5.273522969195818e-4 | 1.987368045333025e-2 | 3.4582563087804954e-2 | 2.296415243198803e-2 |
| user_002 | Sitting | 1.446309394812e12 | -0.612526 | 3.2195 | 9.11964 | -0.584657 | 3.1846636363636365 | 9.157960000000001 | 0.375188100676 | 10.36518025 | 83.1678337296 | 9.690624442226413 | 9.71363099500353 | 2.7869000000000033e-2 | 3.4836363636363554e-2 | 3.8320000000000576e-2 | 3.0719396694214883e-2 | 1.9635041322314164e-2 | 3.4202975206611246e-2 | 7.766811610000018e-4 | 1.213572231404953e-3 | 1.4684224000000442e-3 | 1.4485759797633376e-3 | 6.023731257700965e-4 | 1.390091828700179e-3 | 3.806016263448355e-2 | 2.454329085045639e-2 | 3.728393526306174e-2 |
| user_002 | Sitting | 1.44630939533e12 | -0.612526 | 3.29615 | 9.15796 | -0.5811732727272727 | 3.2020827272727272 | 9.147509090909091 | 0.375188100676 | 10.864604822499999 | 83.86823136159998 | 9.752334299272968 | 9.709300457137386 | 3.135272727272731e-2 | 9.406727272727267e-2 | 1.0450909090907956e-2 | 2.311869421487606e-2 | 3.1670247933884295e-2 | 3.2619504132230824e-2 | 9.829935074380188e-4 | 8.848651798347096e-3 | 1.0922150082642256e-4 | 8.285388598106695e-4 | 1.5844188613072852e-3 | 2.3730853361382037e-3 | 2.8784350953437694e-2 | 3.980475927960481e-2 | 4.871432372658173e-2 |
| user_002 | Sitting | 1.446309395978e12 | -0.574206 | 3.14286 | 9.08132 | -0.5776895454545454 | 3.1986000000000003 | 9.133574545454547 | 0.329712530436 | 9.877568979600001 | 82.47037294239999 | 9.626923415735476 | 9.694850453339638 | 3.4835454545454336e-3 | 5.574000000000012e-2 | 5.2254545454546886e-2 | 2.4385553719008267e-2 | 3.673876033057833e-2 | 3.0085950413222727e-2 | 1.2135088933884152e-5 | 3.1069476000000137e-3 | 2.7305375206613065e-3 | 1.0381688949256207e-3 | 1.9265522670923983e-3 | 1.245566408414738e-3 | 3.222062840674621e-2 | 4.389250809753754e-2 | 3.5292582909369756e-2 |
| user_002 | Sitting | 1.446309396432e12 | -0.535886 | 3.2195 | 9.15796 | -0.574206 | 3.1951154545454545 | 9.123123636363639 | 0.287173804996 | 10.36518025 | 83.86823136159998 | 9.722169789537517 | 9.68355218183141 | 3.832000000000002e-2 | 2.4384545454545492e-2 | 3.4836363636360446e-2 | 2.18519173553719e-2 | 2.4069999999999956e-2 | 2.185190082644617e-2 | 1.4684224000000015e-3 | 5.946060570247952e-4 | 1.2135722314047363e-3 | 7.733775221096924e-4 | 8.297486394440224e-4 | 7.380725661908144e-4 | 2.780966598342548e-2 | 2.880535782530782e-2 | 2.7167490980780952e-2 |
| user_002 | Sitting | 1.446309396496e12 | -0.574206 | 3.2195 | 9.15796 | -0.574206 | 3.198599090909091 | 9.126607272727274 | 0.329712530436 | 10.36518025 | 83.86823136159998 | 9.724357261127132 | 9.687983640247957 | 0.0 | 2.0900909090908915e-2 | 3.1352727272725645e-2 | 2.0901826446280993e-2 | 2.4069752066115665e-2 | 2.2801983471074164e-2 | 0.0 | 4.368480008264389e-4 | 9.829935074379145e-4 | 7.634481220187832e-4 | 8.297382746806869e-4 | 7.877187029301007e-4 | 2.7630564996372824e-2 | 2.88051779144078e-2 | 2.8066326851408623e-2 |
| user_002 | Sitting | 1.446309398634e12 | -0.574206 | 3.18118 | 9.04299 | -0.5951078181818181 | 3.1846636363636365 | 9.10222 | 0.329712530436 | 10.1199061924 | 81.7756681401 | 9.603399755447859 | 9.66175866037553 | 2.0901818181818133e-2 | 3.4836363636365775e-3 | 5.9230000000001226e-2 | 2.9452702479338858e-2 | 3.135297520661163e-2 | 3.3571239669422166e-2 | 4.3688600330578305e-4 | 1.2135722314051077e-5 | 3.5081929000001454e-3 | 1.3647419540728778e-3 | 1.6471628879789681e-3 | 1.5226807697972043e-3 | 3.6942414026060584e-2 | 4.0585254563436805e-2 | 3.902154238106439e-2 |
| user_002 | Sitting | 1.446309398698e12 | -0.535886 | 3.18118 | 9.11964 | -0.5846568181818181 | 3.1846636363636365 | 9.105703636363637 | 0.287173804996 | 10.1199061924 | 83.1678337296 | 9.673412723904423 | 9.664398869095395 | 4.8770818181818165e-2 | 3.4836363636365775e-3 | 1.393636363636297e-2 | 2.7869148760330597e-2 | 3.135289256198352e-2 | 3.04044628099177e-2 | 2.3785927061239654e-3 | 1.2135722314051077e-5 | 1.942222314049401e-4 | 1.182695466860256e-3 | 1.6471623120961733e-3 | 1.3241008296018342e-3 | 3.439033973167837e-2 | 4.058524746870682e-2 | 3.6388196294977776e-2 |
| user_002 | Sitting | 1.446309400253e12 | -0.574206 | 3.14286 | 9.11964 | -0.5776896363636365 | 3.1846645454545457 | 9.116156363636366 | 0.329712530436 | 9.877568979600001 | 83.1678337296 | 9.663080007928942 | 9.673757876165139 | 3.4836363636364664e-3 | 4.180454545454548e-2 | 3.483636363634801e-3 | 7.917404958677664e-3 | 2.850380165289248e-2 | 3.0402644628099426e-2 | 1.2135722314050303e-5 | 1.7476200206611595e-3 | 1.21357223140387e-5 | 1.445261688399688e-4 | 1.1121892084147188e-3 | 1.3989178085650042e-3 | 1.2021903711141958e-2 | 3.334950087204783e-2 | 3.740210968067181e-2 |
| user_002 | Sitting | 1.446309400383e12 | -0.574206 | 3.14286 | 9.15796 | -0.5776896363636365 | 3.1776972727272734 | 9.123123636363637 | 0.329712530436 | 9.877568979600001 | 83.86823136159998 | 9.699253212058956 | 9.67803402396883 | 3.4836363636364664e-3 | 3.4837272727273216e-2 | 3.483636363636222e-2 | 6.967305785123975e-3 | 2.850396694214874e-2 | 3.008595041322305e-2 | 1.2135722314050303e-5 | 1.2136355710744143e-3 | 1.2135722314048601e-3 | 1.323901586205852e-4 | 1.1673573407963887e-3 | 1.3757496114199747e-3 | 1.1506092239356731e-2 | 3.4166611491284714e-2 | 3.7091098816562106e-2 |
| user_002 | Sitting | 1.446309400901e12 | -0.535886 | 3.18118 | 9.11964 | -0.5707223636363635 | 3.177696363636364 | 9.137058181818183 | 0.287173804996 | 10.1199061924 | 83.1678337296 | 9.673412723904423 | 9.690800477019932 | 3.4836363636363554e-2 | 3.4836363636356893e-3 | 1.7418181818182887e-2 | 2.4385652892562e-2 | 2.5019090909090904e-2 | 3.0085950413222727e-2 | 1.213572231404953e-3 | 1.2135722314044888e-5 | 3.0339305785127694e-4 | 1.3095801557205114e-3 | 9.454957610067568e-4 | 1.3073482674680103e-3 | 3.618812174900089e-2 | 3.0748914793968858e-2 | 3.6157271294554434e-2 |
| user_002 | Standing | 1.446034734537e12 | -1.37893 | -9.46452 | 0.152682 | -1.4590536363636366 | -9.412265454545455 | 0.14223145454545455 | 1.9014479449 | 89.5771388304 | 2.3311793124000002e-2 | 9.565662474100996 | 9.526415292004481 | 8.012363636363662e-2 | 5.225454545454511e-2 | 1.0450545454545462e-2 | 5.9618691328873166e-2 | 4.260542568542598e-2 | 6.1788887987012986e-2 | 6.419797104132272e-3 | 2.730537520661121e-3 | 1.0921390029752082e-4 | 4.641813759714882e-3 | 2.3892717890542994e-3 | 5.730558008145936e-3 | 6.813085761763815e-2 | 4.888017787461804e-2 | 7.570044919381877e-2 |
| user_002 | Standing | 1.446034735314e12 | -1.41725 | -9.4262 | 0.114362 | -1.4242172727272726 | -9.408781818181817 | 0.1491988181818182 | 2.0085975625 | 88.85324643999999 | 1.3078667044000002e-2 | 9.53283392646405 | 9.51766258066411 | 6.967272727272711e-3 | 1.7418181818182887e-2 | 3.4836818181818205e-2 | 3.9903471074380255e-2 | 3.673652892561983e-2 | 6.333939669421487e-2 | 4.854288925619812e-5 | 3.0339305785127694e-4 | 1.2136039010330595e-3 | 2.846378506386181e-3 | 1.9505415501126994e-3 | 7.261695278080391e-3 | 5.335146208292872e-2 | 4.41649357535217e-2 | 8.521558119311509e-2 |
| user_002 | Standing | 1.446034736027e12 | -1.41725 | -9.46452 | 0.152682 | -1.4067990909090906 | -9.408781818181815 | 0.15616600000000003 | 2.0085975625 | 89.5771388304 | 2.3311793124000002e-2 | 9.571261577557266 | 9.515199605370448 | 1.0450909090909288e-2 | 5.573818181818524e-2 | 3.484000000000015e-3 | 2.2801983471074386e-2 | 3.420297520661222e-2 | 7.410701652892565e-2 | 1.0922150082645041e-4 | 3.1067449123970757e-3 | 1.2138256000000103e-5 | 7.810992180315554e-4 | 1.4695256474831387e-3 | 9.435107387730278e-3 | 2.7948152318741135e-2 | 3.833439248877095e-2 | 9.713448094127172e-2 |
| user_002 | Standing | 1.446034736285e12 | -1.37893 | -9.34956 | 0.191003 | -1.3719627272727273 | -9.419232727272725 | 0.17358436363636362 | 1.9014479449 | 87.41427219360001 | 3.6482146009e-2 | 9.4526293847008 | 9.520563831918595 | 6.967272727272711e-3 | 6.967272727272444e-2 | 1.7418636363636386e-2 | 4.5287272727272725e-2 | 2.945256198347175e-2 | 3.958722314049588e-2 | 4.854288925619812e-5 | 4.8542889256194405e-3 | 3.0340889276859583e-4 | 3.873501913148003e-3 | 1.2720443480090616e-3 | 2.2782432499729535e-3 | 6.223746390356859e-2 | 3.566573072304929e-2 | 4.7730946460058314e-2 |
| user_002 | Standing | 1.446034737451e12 | -1.37893 | -9.46452 | 0.191003 | -1.3824136363636361 | -9.422716363636363 | 0.18403536363636366 | 1.9014479449 | 89.5771388304 | 3.6482146009e-2 | 9.56635086756225 | 9.525611176471477 | 3.4836363636361334e-3 | 4.180363636363715e-2 | 6.9676363636363425e-3 | 4.623735537190076e-2 | 3.166942148760428e-2 | 2.7236190082644628e-2 | 1.2135722314047982e-5 | 1.7475440132232066e-3 | 4.8547956495867476e-5 | 3.252373580165281e-3 | 1.456286677685994e-3 | 1.25110982419459e-3 | 5.7029585130573074e-2 | 3.816132437017869e-2 | 3.5371030861350225e-2 |
| user_002 | Standing | 1.446034737646e12 | -1.30229 | -9.38788 | 0.152682 | -1.3824136363636361 | -9.40529818181818 | 0.1910026363636364 | 1.6959592440999998 | 88.13229089439999 | 2.3311793124000002e-2 | 9.479006378920946 | 9.508497146805437 | 8.012363636363617e-2 | 1.741818181818111e-2 | 3.832063636363639e-2 | 4.3387107438016465e-2 | 3.0719338842975154e-2 | 3.166987603305785e-2 | 6.419797104132201e-3 | 3.0339305785121503e-4 | 1.4684711713140515e-3 | 3.012968876333576e-3 | 1.3272067221637865e-3 | 1.7299211320007516e-3 | 5.489051718041629e-2 | 3.643084849634697e-2 | 4.159232058927166e-2 |
| user_002 | Standing | 1.446034738163e12 | -1.37893 | -9.38788 | 0.267643 | -1.3510609090909094 | -9.401814545454544 | 0.20842090909090913 | 1.9014479449 | 88.13229089439999 | 7.163277544900001e-2 | 9.492384927653797 | 9.500906714561507 | 2.786909090909062e-2 | 1.3934545454544534e-2 | 5.922209090909089e-2 | 2.9135867768595093e-2 | 2.881917355371868e-2 | 4.433765289256198e-2 | 7.766862280991575e-4 | 1.941715570247677e-4 | 3.507256051644626e-3 | 1.782847932682195e-3 | 1.1462741349361407e-3 | 2.7228712753749056e-3 | 4.2223783969253574e-2 | 3.385667046441721e-2 | 5.2181139077016186e-2 |
| user_002 | Standing | 1.446034739847e12 | -1.37893 | -9.4262 | 0.305964 | -1.3580281818181819 | -9.419232727272727 | 0.2293230909090909 | 1.9014479449 | 88.85324643999999 | 9.361396929600001e-2 | 9.531437895417248 | 9.519686143429096 | 2.0901818181818133e-2 | 6.967272727273155e-3 | 7.664090909090912e-2 | 4.433719008264456e-2 | 4.497057851239703e-2 | 4.1170512396694205e-2 | 4.3688600330578305e-4 | 4.854288925620431e-5 | 5.8738289462809965e-3 | 2.848585001352366e-3 | 2.9280188201352667e-3 | 2.2418373498437265e-3 | 5.337213693822242e-2 | 5.4111170936649175e-2 | 4.734804483654765e-2 |
| user_002 | Standing | 1.446034740883e12 | -1.41725 | -9.50284 | 0.229323 | -1.399831818181818 | -9.433167272727273 | 0.21538836363636363 | 2.0085975625 | 90.30396806560002 | 5.2589038329e-2 | 9.610679199017572 | 9.539154241075058 | 1.7418181818182e-2 | 6.9672727272728e-2 | 1.3934636363636371e-2 | 4.560396694214874e-2 | 4.497057851239719e-2 | 3.515331404958677e-2 | 3.03393057851246e-4 | 4.854288925619936e-3 | 1.9417409058677707e-4 | 2.7514992228399723e-3 | 3.0251045986476096e-3 | 1.8965196208602549e-3 | 5.2454734989703e-2 | 5.500095088857655e-2 | 4.354904844953854e-2 |
| user_002 | Standing | 1.446034741272e12 | -1.49389 | -9.38788 | 0.229323 | -1.4067990909090908 | -9.422716363636361 | 0.22932300000000003 | 2.2317073320999996 | 88.13229089439999 | 5.2589038329e-2 | 9.508763708539034 | 9.53024742165128 | 8.709090909090911e-2 | 3.483636363636222e-2 | 2.7755575615628914e-17 | 4.6870743801652924e-2 | 3.135272727272775e-2 | 4.02205041322314e-2 | 7.584826446280995e-3 | 1.2135722314048601e-3 | 7.703719777548943e-34 | 2.989800679188584e-3 | 1.6140510677686065e-3 | 2.4305041856851994e-3 | 5.467906984567847e-2 | 4.017525442070786e-2 | 4.930014387083672e-2 |
| user_002 | Standing | 1.446034742059e12 | -1.95374 | -12.4535 | 0.305964 | -1.434669090909091 | -9.70140909090909 | 0.22583936363636362 | 3.8170999876000002 | 155.08966225 | 9.361396929600001e-2 | 12.609535130483438 | 9.809795217760076 | 0.5190709090909091 | 2.75209090909091 | 8.01246363636364e-2 | 8.012446280991732e-2 | 0.27267537190082775 | 4.877147933884297e-2 | 0.26943460866446284 | 7.574004371900831 | 6.419957352404964e-3 | 2.633868512486852e-2 | 0.6893434999212628 | 3.2767264445206614e-3 | 0.16229197492441985 | 0.8302671256416593 | 5.724269773971752e-2 |
| user_002 | Standing | 1.446034742189e12 | -1.30229 | -9.38788 | 0.229323 | -1.319708181818182 | -9.380912727272728 | 0.1248130909090909 | 1.6959592440999998 | 88.13229089439999 | 5.2589038329e-2 | 9.480550573507267 | 9.474632844510081 | 1.7418181818182e-2 | 6.967272727271379e-3 | 0.1045099090909091 | 6.840619834710743e-2 | 0.14821371900826383 | 9.659252066115703e-2 | 3.03393057851246e-4 | 4.8542889256179554e-5 | 1.0922321098190083e-2 | 7.480087032682192e-3 | 3.0882480126521214e-2 | 1.2423861191243425e-2 | 8.648749639504077e-2 | 0.17573411770774966 | 0.11146237567557685 |
| user_002 | Standing | 1.446034742213e12 | -1.22565 | -9.46452 | 0.420925 | -1.319708181818182 | -9.370461818181816 | 0.1770680909090909 | 1.5022179224999999 | 89.5771388304 | 0.177177855625 | 9.552828618190793 | 9.465620992624615 | 9.405818181818204e-2 | 9.405818181818404e-2 | 0.2438569090909091 | 6.777256198347112e-2 | 0.10609289256198341 | 9.817609917355372e-2 | 8.84694156694219e-3 | 8.846941566942566e-3 | 5.9466192111371906e-2 | 6.401056292261465e-3 | 2.082176824943647e-2 | 1.4755099993800152e-2 | 8.000660155425592e-2 | 0.1442974991101248 | 0.12147057254248929 |
| user_002 | Standing | 1.446034742229e12 | -1.37893 | -9.4262 | 0.229323 | -1.3231918181818183 | -9.37742909090909 | 0.20493736363636367 | 1.9014479449 | 88.85324643999999 | 5.2589038329e-2 | 9.529285567304036 | 9.473261574063345 | 5.573818181818169e-2 | 4.877090909091031e-2 | 2.438563636363633e-2 | 6.0488595041322334e-2 | 6.30221487603309e-2 | 7.727414049586777e-2 | 3.1067449123966797e-3 | 2.378601573553838e-3 | 5.946592608595026e-4 | 5.005433830803912e-3 | 7.767965528474898e-3 | 1.073259264615402e-2 | 7.074909067121578e-2 | 8.813606258776766e-2 | 0.10359822704155713 |
| user_002 | Standing | 1.446034742479e12 | -1.49389 | -9.38788 | 0.191003 | -1.3266754545454547 | -9.436650909090908 | 0.20145363636363633 | 2.2317073320999996 | 88.13229089439999 | 3.6482146009e-2 | 9.507916720949389 | 9.53247609161914 | 0.16721454545454528 | 4.877090909090853e-2 | 1.0450636363636329e-2 | 7.568991735537184e-2 | 7.822347107438028e-2 | 6.333947933884297e-2 | 2.796070421157019e-2 | 2.3786015735536644e-3 | 1.0921580040495795e-4 | 9.003602709541687e-3 | 1.141971469752076e-2 | 7.45809411693238e-3 | 9.488731585170743e-2 | 0.10686306516996769 | 8.636025774007614e-2 |
| user_002 | Standing | 1.446034742496e12 | -1.45557 | -9.57948 | 0.382604 | -1.3336427272727271 | -9.461036363636364 | 0.22235554545454547 | 2.1186840249000003 | 91.7664370704 | 0.146385820816 | 9.696984423835897 | 9.558129142000816 | 0.12192727272727288 | 0.11844363636363653 | 0.16024845454545453 | 8.392396694214872e-2 | 8.99411570247939e-2 | 6.808996694214875e-2 | 1.4866259834710783e-2 | 1.4028894995041362e-2 | 2.5679567184206605e-2 | 1.0234826900676175e-2 | 1.2855039673027917e-2 | 8.73236787716153e-3 | 0.10116732130819801 | 0.1133800673532518 | 9.344713948089332e-2 |
| user_002 | Standing | 1.446034742504e12 | -1.41725 | -9.38788 | 0.267643 | -1.3440936363636364 | -9.461036363636362 | 0.2328065454545455 | 2.0085975625 | 88.13229089439999 | 7.163277544900001e-2 | 9.498027228448494 | 9.559858310139685 | 7.315636363636346e-2 | 7.31563636363628e-2 | 3.483645454545453e-2 | 8.614082644628097e-2 | 9.437487603305815e-2 | 6.872330578512396e-2 | 5.351853540495843e-3 | 5.351853540495745e-3 | 1.2135785652975196e-3 | 1.0505122534034553e-2 | 1.3287512686401303e-2 | 8.772082137801653e-3 | 0.10249450001846222 | 0.11527147386236242 | 9.365939428483216e-2 |
| user_002 | Standing | 1.446034742697e12 | -1.41725 | -9.54116 | 0.229323 | -1.330159090909091 | -9.440134545454546 | 0.20493754545454543 | 2.0085975625 | 91.0337341456 | 5.2589038329e-2 | 9.648570917313558 | 9.536511487088848 | 8.709090909090889e-2 | 0.10102545454545364 | 2.438545454545457e-2 | 0.300228347107438 | 0.9735261983471073 | 8.51913388429752e-2 | 7.584826446280956e-3 | 1.0206142466115519e-2 | 5.94650393388431e-4 | 0.10673193306093162 | 1.1308478302546208 | 1.2798934184031554e-2 | 0.3266985354435058 | 1.0634132923067214 | 0.11313237460617342 |
| user_002 | Standing | 1.446034742745e12 | -1.41725 | -9.34956 | 3.7722e-2 | -1.3754463636363636 | -9.37742909090909 | 0.1701008181818182 | 2.0085975625 | 87.41427219360001 | 1.422949284e-3 | 9.456441862845878 | 9.479904604626066 | 4.1803636363636265e-2 | 2.7869090909089067e-2 | 0.13237881818181818 | 0.12351148760330578 | 0.383202727272727 | 6.42894214876033e-2 | 1.7475440132231322e-3 | 7.766862280990708e-4 | 1.7524151503214874e-2 | 2.7764770109316318e-2 | 0.39587592896852003 | 7.51654394957025e-3 | 0.16662763909182748 | 0.6291867202734972 | 8.669800429981217e-2 |
| user_002 | Standing | 1.446034742843e12 | -1.37893 | -9.54116 | 0.229323 | -1.3406099999999999 | -9.44710181818182 | 0.2014538181818182 | 1.9014479449 | 91.0337341456 | 5.2589038329e-2 | 9.643016702714405 | 9.544607831899343 | 3.832000000000013e-2 | 9.405818181818049e-2 | 2.7869181818181793e-2 | 6.967272727272725e-2 | 8.740760330578484e-2 | 7.537393388429751e-2 | 1.4684224000000102e-3 | 8.846941566941898e-3 | 7.766912952148746e-4 | 8.164031374906068e-3 | 1.0807412344402613e-2 | 8.36277162100526e-3 | 9.035502960492055e-2 | 0.10395870499579443 | 9.144819091160448e-2 |
| user_002 | Standing | 1.446034743054e12 | -1.34061 | -9.65612 | 7.6042e-2 | -1.3092572727272727 | -9.44710181818182 | 0.15616609090909092 | 1.7972351721000002 | 93.24065345439999 | 5.782385764e-3 | 9.749034363067144 | 9.539196670106646 | 3.135272727272742e-2 | 0.20901818181818044 | 8.012409090909092e-2 | 7.56899917355372e-2 | 8.582413223140538e-2 | 5.478866942148761e-2 | 9.829935074380258e-4 | 4.3688600330577934e-2 | 6.4198699440082664e-3 | 8.36373350696845e-3 | 1.0887949410668648e-2 | 5.546132543806912e-3 | 9.145344994568794e-2 | 0.10434533727325168 | 7.447236093885377e-2 |
| user_002 | Standing | 1.446034743119e12 | -1.34061 | -9.50284 | 0.305964 | -1.3406099999999999 | -9.44710181818182 | 0.22235572727272726 | 1.7972351721000002 | 90.30396806560002 | 9.361396929600001e-2 | 9.60181322495892 | 9.545211176307818 | 2.220446049250313e-16 | 5.573818181818169e-2 | 8.360827272727275e-2 | 7.885685950413222e-2 | 0.1285778512396694 | 7.347359504132234e-2 | 4.930380657631324e-32 | 3.1067449123966797e-3 | 6.9903432684380205e-3 | 9.559639441021782e-3 | 2.279750599068361e-2 | 6.198137410960935e-3 | 9.77734086601351e-2 | 0.15098842998946513 | 7.872825039946547e-2 |
| user_002 | Standing | 1.446034743217e12 | -1.30229 | -9.46452 | 7.6042e-2 | -1.2674536363636362 | -9.37742909090909 | 0.18055172727272728 | 1.6959592440999998 | 89.5771388304 | 5.782385764e-3 | 9.553998140059688 | 9.465772326184897 | 3.4836363636363776e-2 | 8.709090909091088e-2 | 0.10450972727272728 | 8.170710743801644e-2 | 0.1045090909090907 | 9.405892561983471e-2 | 1.2135722314049683e-3 | 7.584826446281304e-3 | 1.0922283094619836e-2 | 1.2351958820736288e-2 | 1.3108786594139726e-2 | 1.4581873210133732e-2 | 0.11113936665617763 | 0.11449360940305675 | 0.12075542724918716 |
| user_002 | Standing | 1.446034743361e12 | -1.34061 | -9.4262 | 7.6042e-2 | -1.2117153636363636 | -9.408781818181817 | 0.2188720909090909 | 1.7972351721000002 | 88.85324643999999 | 5.782385764e-3 | 9.521358306348102 | 9.489841144717666 | 0.12889463636363652 | 1.7418181818182887e-2 | 0.1428300909090909 | 7.252305785123975e-2 | 6.492231404958737e-2 | 7.727408264462808e-2 | 1.661382728331409e-2 | 3.0339305785127694e-4 | 2.0400434869099173e-2 | 1.0605562795334355e-2 | 8.118798228099232e-3 | 8.248014420332078e-3 | 0.10298331318876061 | 9.010437407861636e-2 | 9.081857970884635e-2 |
| user_002 | Standing | 1.446034743629e12 | -1.30229 | -9.4262 | 0.191003 | -1.2186827272727272 | -9.461036363636365 | 0.2362904545454545 | 1.6959592440999998 | 88.85324643999999 | 3.6482146009e-2 | 9.517651382043208 | 9.542495937109814 | 8.360727272727275e-2 | 3.4836363636365775e-2 | 4.528745454545449e-2 | 6.270545454545462e-2 | 6.840595041322298e-2 | 4.908799999999998e-2 | 6.990176052892566e-3 | 1.2135722314051078e-3 | 2.0509535392066068e-3 | 5.247045029601812e-3 | 7.79775321051837e-3 | 3.6131960248234384e-3 | 7.243648962782372e-2 | 8.830488780649896e-2 | 6.010986628519014e-2 |
| user_002 | Standing | 1.446034743734e12 | -1.30229 | -9.54116 | 0.267643 | -1.2848718181818182 | -9.419232727272727 | 0.2746105454545455 | 1.6959592440999998 | 91.0337341456 | 7.163277544900001e-2 | 9.63334449530115 | 9.510983749677504 | 1.7418181818181777e-2 | 0.1219272727272731 | 6.967545454545476e-3 | 4.750413223140497e-2 | 8.075702479338838e-2 | 5.447178512396693e-2 | 3.0339305785123823e-4 | 1.4866259834710837e-2 | 4.8546689661157324e-5 | 4.470358801502632e-3 | 1.3350397792937658e-2 | 5.0154347465409446e-3 | 6.686074185576041e-2 | 0.11554392148848705 | 7.0819734160338e-2 |
| user_002 | Standing | 1.446034743863e12 | -1.26397 | -9.4262 | 0.191003 | -1.2744208181818182 | -9.384396363636363 | 0.15964990909090912 | 1.5976201609 | 88.85324643999999 | 3.6482146009e-2 | 9.512483836880302 | 9.473333030001008 | 1.0450818181818144e-2 | 4.180363636363715e-2 | 3.1353090909090886e-2 | 9.817528099173557e-2 | 9.152462809917351e-2 | 8.962506611570248e-2 | 1.0921960066942071e-4 | 1.7475440132232066e-3 | 9.830163095537175e-4 | 1.8574320517341106e-2 | 1.3710056472426733e-2 | 1.0056230595943651e-2 | 0.1362876389014833 | 0.11708995034769949 | 0.10028075885205323 |
| user_002 | Standing | 1.446034744017e12 | -1.18733 | -9.4262 | 0.191003 | -1.2535190909090907 | -9.468003636363635 | 0.19448636363636365 | 1.4097525289 | 88.85324643999999 | 3.6482146009e-2 | 9.50260391234471 | 9.553536110914292 | 6.618909090909075e-2 | 4.180363636363538e-2 | 3.4833636363636455e-3 | 8.012364462809915e-2 | 7.980694214876038e-2 | 8.234113223140495e-2 | 4.38099575537188e-3 | 1.747544013223058e-3 | 1.213382222314056e-5 | 8.765301886582264e-3 | 1.0158702824342671e-2 | 1.0075026109997748e-2 | 9.362319096560565e-2 | 0.10079039053571859 | 0.10037442956250237 |
| user_002 | Standing | 1.446034744033e12 | -1.14901 | -9.4262 | 0.114362 | -1.2291336363636363 | -9.474970909090906 | 0.22235572727272726 | 1.3202239801000002 | 88.85324643999999 | 1.3078667044000002e-2 | 9.496659891095605 | 9.557664967676123 | 8.012363636363617e-2 | 4.8770909090906756e-2 | 0.10799372727272726 | 7.537322314049583e-2 | 8.7407603305785e-2 | 7.56904958677686e-2 | 6.419797104132201e-3 | 2.3786015735534913e-3 | 1.1662645130256194e-2 | 7.338802257550711e-3 | 1.0555871918256998e-2 | 8.152030046724268e-3 | 8.566680954459965e-2 | 0.10274177299549098 | 9.028859311521178e-2 |
| user_002 | Standing | 1.446034744122e12 | -1.34061 | -9.4262 | 0.229323 | -1.18733 | -9.440134545454546 | 0.25022509090909084 | 1.7972351721000002 | 88.85324643999999 | 5.2589038329e-2 | 9.523815971050102 | 9.518284053408653 | 0.15328000000000008 | 1.393454545454631e-2 | 2.0902090909090842e-2 | 6.207206611570248e-2 | 6.492231404958737e-2 | 5.605552892561983e-2 | 2.3494758400000024e-2 | 1.9417155702481723e-4 | 4.3689740437189806e-4 | 5.383847717505636e-3 | 5.835075938091722e-3 | 3.666180406767093e-3 | 7.337470761444734e-2 | 7.638766875675498e-2 | 6.05489917898481e-2 |
| user_002 | Standing | 1.446034744179e12 | -1.11069 | -9.50284 | 0.229323 | -1.1942972727272727 | -9.4262 | 0.2676434545454545 | 1.2336322760999998 | 90.30396806560002 | 5.2589038329e-2 | 9.570276348153643 | 9.505986245872176 | 8.360727272727275e-2 | 7.664000000000115e-2 | 3.832045454545452e-2 | 7.85401652892562e-2 | 5.637157024793444e-2 | 5.605547107438018e-2 | 6.990176052892566e-3 | 5.873689600000177e-3 | 1.4684572365702459e-3 | 8.300834062809922e-3 | 4.691008298121736e-3 | 4.023622975179566e-3 | 9.110891319080654e-2 | 6.849093588294539e-2 | 6.34320342979757e-2 |
| user_002 | Standing | 1.446034744284e12 | -1.30229 | -9.54116 | 0.114362 | -1.2848718181818182 | -9.412265454545455 | 0.218872 | 1.6959592440999998 | 91.0337341456 | 1.3078667044000002e-2 | 9.630304878701608 | 9.502988396067709 | 1.7418181818181777e-2 | 0.12889454545454448 | 0.10451 | 6.713917355371905e-2 | 5.8271735537191074e-2 | 6.048900000000001e-2 | 3.0339305785123823e-4 | 1.6613803847933633e-2 | 1.09223401e-2 | 1.2435805629451542e-2 | 6.034763732531968e-3 | 5.535077266789631e-3 | 0.11151594338681596 | 7.76837417516173e-2 | 7.4398099886957e-2 |
| user_002 | Standing | 1.446034744308e12 | -1.30229 | -9.38788 | 0.267643 | -1.3092572727272727 | -9.436650909090908 | 0.2223556363636364 | 1.6959592440999998 | 88.13229089439999 | 7.163277544900001e-2 | 9.481554878496933 | 9.530499628943828 | 6.967272727272711e-3 | 4.877090909090853e-2 | 4.5287363636363626e-2 | 6.17553719008265e-2 | 5.700495867768654e-2 | 6.365594214876034e-2 | 4.854288925619812e-5 | 2.3786015735536644e-3 | 2.0509453051322304e-3 | 1.2132412571600304e-2 | 5.463281536288513e-3 | 5.738075321901578e-3 | 0.11014723133878719 | 7.391401447823351e-2 | 7.575008463296644e-2 |
| user_002 | Standing | 1.446034744341e12 | -1.14901 | -9.46452 | 0.305964 | -1.2709372727272727 | -9.436650909090908 | 0.2293230909090909 | 1.3202239801000002 | 89.5771388304 | 9.361396929600001e-2 | 9.538919057199092 | 9.525312959546817 | 0.12192727272727266 | 2.786909090909262e-2 | 7.664090909090912e-2 | 5.5421487603305834e-2 | 5.7955041322314216e-2 | 7.632393388429752e-2 | 1.4866259834710727e-2 | 7.766862280992689e-4 | 5.8738289462809965e-3 | 5.296691166341102e-3 | 6.013802030353122e-3 | 7.328998268961682e-3 | 7.277837018195106e-2 | 7.754870747055119e-2 | 8.560956879322358e-2 |
| user_002 | Standing | 1.446034744446e12 | -1.26397 | -9.38788 | 0.305964 | -1.2012645454545454 | -9.422716363636363 | 0.250225 | 1.5976201609 | 88.13229089439999 | 9.361396929600001e-2 | 9.477527368707305 | 9.502884756149422 | 6.270545454545462e-2 | 3.4836363636364e-2 | 5.573900000000004e-2 | 6.840595041322314e-2 | 5.2254545454546726e-2 | 6.840666115702475e-2 | 3.931974029752075e-3 | 1.213572231404984e-3 | 3.1068361210000043e-3 | 6.374563957325324e-3 | 4.255225542299172e-3 | 7.1072621082915064e-3 | 7.984086646151407e-2 | 6.52320898201121e-2 | 8.43045794028504e-2 |
| user_002 | Standing | 1.446034744486e12 | -1.18733 | -9.46452 | 0.152682 | -1.2395845454545453 | -9.436650909090908 | 0.2537086363636364 | 1.4097525289 | 89.5771388304 | 2.3311793124000002e-2 | 9.53992678967842 | 9.521476367134994 | 5.225454545454533e-2 | 2.786909090909262e-2 | 0.10102663636363637 | 6.555570247933881e-2 | 4.7504132231405774e-2 | 5.003823140495869e-2 | 2.7305375206611443e-3 | 7.766862280992689e-4 | 1.0206381254950415e-2 | 5.3342015807663415e-3 | 3.135429346957241e-3 | 3.698171704709241e-3 | 7.303561857591363e-2 | 5.59949046517381e-2 | 6.081259495128654e-2 |
| user_002 | Standing | 1.446034744527e12 | -1.30229 | -9.34956 | 0.191003 | -1.2570027272727275 | -9.419232727272728 | 0.22235572727272726 | 1.6959592440999998 | 87.41427219360001 | 3.6482146009e-2 | 9.441753734540475 | 9.505965099520607 | 4.52872727272724e-2 | 6.9672727272728e-2 | 3.1352727272727254e-2 | 4.782082644628092e-2 | 6.112198347107474e-2 | 8.044102479338842e-2 | 2.0509370710743505e-3 | 4.854288925619936e-3 | 9.829935074380154e-4 | 2.7812869048835388e-3 | 5.221670337490657e-3 | 9.563103558231404e-3 | 5.273790766501397e-2 | 7.226112604637888e-2 | 9.779112208289362e-2 |
| user_002 | Standing | 1.446034744543e12 | -1.26397 | -9.46452 | 0.267643 | -1.2709372727272727 | -9.433167272727273 | 0.21538827272727276 | 1.5976201609 | 89.5771388304 | 7.163277544900001e-2 | 9.552297721844154 | 9.521519978230494 | 6.967272727272711e-3 | 3.135272727272742e-2 | 5.225472727272726e-2 | 5.067107438016521e-2 | 5.478809917355418e-2 | 8.012421487603305e-2 | 4.854288925619812e-5 | 9.829935074380258e-4 | 2.730556522347106e-3 | 4.0555377478587425e-3 | 4.760512889556786e-3 | 9.528890679027796e-3 | 6.368310410037141e-2 | 6.899647012388957e-2 | 9.761603699714405e-2 |
| user_002 | Standing | 1.446034744875e12 | -1.14901 | -9.4262 | 0.152682 | -1.2186827272727274 | -9.46452 | 0.24674118181818178 | 1.3202239801000002 | 88.85324643999999 | 2.3311793124000002e-2 | 9.497198650824567 | 9.547161596295055 | 6.967272727272733e-2 | 3.8320000000000576e-2 | 9.405918181818176e-2 | 0.10767603305785124 | 9.880859504132208e-2 | 5.5422041322314035e-2 | 4.854288925619843e-3 | 1.4684224000000442e-3 | 8.847129684305776e-3 | 1.593530664583021e-2 | 1.4145839228249378e-2 | 5.6145228321359845e-3 | 0.12623512445365676 | 0.11893628221972208 | 7.493011965915966e-2 |
| user_002 | Standing | 1.446034744956e12 | -1.26397 | -9.57948 | 0.305964 | -1.2988063636363636 | -9.450585454545454 | 0.29899645454545454 | 1.5976201609 | 91.7664370704 | 9.361396929600001e-2 | 9.667350785018407 | 9.545155603286355 | 3.4836363636363554e-2 | 0.12889454545454626 | 6.967545454545476e-3 | 8.804099173553723e-2 | 7.759008264462866e-2 | 8.044104958677685e-2 | 1.213572231404953e-3 | 1.661380384793409e-2 | 4.8546689661157324e-5 | 1.2332100366040578e-2 | 1.05988985700978e-2 | 8.84379644874906e-3 | 0.11104999039189774 | 0.10295095225444882 | 9.404146132823044e-2 |
| user_002 | Standing | 1.446034745005e12 | -1.45557 | -9.46452 | 0.420925 | -1.3580281818181819 | -9.412265454545455 | 0.25719245454545453 | 2.1186840249000003 | 89.5771388304 | 0.177177855625 | 9.585040464751572 | 9.514294265813772 | 9.754181818181817e-2 | 5.225454545454511e-2 | 0.16373254545454546 | 7.632330578512402e-2 | 8.107371900826492e-2 | 9.817595041322313e-2 | 9.514406294214874e-3 | 2.730537520661121e-3 | 2.6808346441024797e-2 | 9.358848399098426e-3 | 1.2925647511946055e-2 | 1.5820824567277233e-2 | 9.674114119183433e-2 | 0.11369101772763782 | 0.12578085930409774 |
| user_002 | Standing | 1.446034745021e12 | -1.22565 | -9.34956 | 0.305964 | -1.34061 | -9.41574909090909 | 0.2676434545454546 | 1.5022179224999999 | 87.41427219360001 | 9.361396929600001e-2 | 9.434516632313285 | 9.515562287015113 | 0.11496000000000017 | 6.618909090908964e-2 | 3.832054545454544e-2 | 5.9538512396694276e-2 | 7.157289256198367e-2 | 9.690919834710741e-2 | 1.321580160000004e-2 | 4.380995755371733e-3 | 1.4684642039338833e-3 | 5.798668771149516e-3 | 8.358202931930932e-3 | 1.574139241835687e-2 | 7.614899061149476e-2 | 9.14232078409576e-2 | 0.12546470586725522 |
| user_002 | Standing | 1.446034745029e12 | -1.30229 | -9.38788 | 0.344284 | -1.3440936363636364 | -9.412265454545455 | 0.2676434545454546 | 1.6959592440999998 | 88.13229089439999 | 0.11853147265599999 | 9.484027710374743 | 9.51258338995772 | 4.180363636363649e-2 | 2.4385454545456042e-2 | 7.66405454545454e-2 | 6.17553719008265e-2 | 7.283966942148803e-2 | 9.595910743801653e-2 | 1.7475440132231508e-3 | 5.946503933885027e-4 | 5.87377320757024e-3 | 5.92995522163787e-3 | 8.402332831254758e-3 | 1.5585832047257697e-2 | 7.700620248809747e-2 | 9.16642396535026e-2 | 0.12484322988155064 |
| user_002 | Standing | 1.446034745191e12 | -0.880768 | -9.46452 | 0.344284 | -1.1942970909090909 | -9.454069090909089 | 0.25719245454545453 | 0.775752269824 | 89.5771388304 | 0.11853147265599999 | 9.51164668040608 | 9.534122835622195 | 0.31352909090909087 | 1.0450909090911509e-2 | 8.709154545454545e-2 | 7.790694214876029e-2 | 7.790677685950408e-2 | 9.310898347107441e-2 | 9.830049084628097e-2 | 1.0922150082649681e-4 | 7.584937289661156e-3 | 1.191517646311044e-2 | 7.166695650187797e-3 | 1.3993826639531935e-2 | 0.10915666018668051 | 8.465633851158338e-2 | 0.11829550557621339 |
| user_002 | Standing | 1.446034745272e12 | -1.18733 | -9.54116 | 0.152682 | -1.2221661818181817 | -9.394847272727272 | 0.23629027272727274 | 1.4097525289 | 91.0337341456 | 2.3311793124000002e-2 | 9.615965810443795 | 9.478361666848208 | 3.483618181818171e-2 | 0.14631272727272737 | 8.360827272727273e-2 | 9.722535537190079e-2 | 8.234049586776897e-2 | 6.80899090909091e-2 | 1.213559563669414e-3 | 2.14074141619835e-2 | 6.990343268438016e-3 | 1.6448434766371138e-2 | 1.2261492527122527e-2 | 6.115423331329075e-3 | 0.1282514513226698 | 0.11073162388009365 | 7.820117218641338e-2 |
| user_002 | Standing | 1.446034745296e12 | -1.30229 | -9.4262 | 0.267643 | -1.2639699999999998 | -9.422716363636363 | 0.23977390909090912 | 1.6959592440999998 | 88.85324643999999 | 7.163277544900001e-2 | 9.519497805007836 | 9.51088135613942 | 3.832000000000013e-2 | 3.4836363636365775e-3 | 2.78690909090909e-2 | 7.600667768595039e-2 | 0.11147636363636386 | 5.6688743801652904e-2 | 1.4684224000000102e-3 | 1.2135722314051077e-5 | 7.76686228099173e-4 | 8.528118130512387e-3 | 1.886994495086403e-2 | 4.8312134340030045e-3 | 9.23478106427672e-2 | 0.13736791820095415 | 6.950693083429166e-2 |
| user_002 | Standing | 1.446034745304e12 | -1.11069 | -9.34956 | 0.267643 | -1.2500354545454544 | -9.401814545454544 | 0.2537085454545455 | 1.2336322760999998 | 87.41427219360001 | 7.163277544900001e-2 | 9.419104906791782 | 9.48869229648534 | 0.13934545454545444 | 5.225454545454333e-2 | 1.39344545454545e-2 | 8.297393388429747e-2 | 0.10482578512396691 | 4.8137900826446284e-2 | 1.941715570247931e-2 | 2.7305375206609353e-3 | 1.9416902347933756e-4 | 9.935859846031542e-3 | 1.7688366896468802e-2 | 3.7886246970232906e-3 | 9.967878332941038e-2 | 0.13299761989024014 | 6.1551804985908336e-2 |
| user_002 | Standing | 1.44603474532e12 | -1.30229 | -9.34956 | 0.344284 | -1.2500354545454544 | -9.422716363636361 | 0.2850615454545455 | 1.6959592440999998 | 87.41427219360001 | 0.11853147265599999 | 9.4460977609993 | 9.510255819754057 | 5.225454545454555e-2 | 7.315636363636102e-2 | 5.9222454545454495e-2 | 7.53732396694215e-2 | 8.234049586776816e-2 | 5.067149586776858e-2 | 2.7305375206611673e-3 | 5.351853540495485e-3 | 3.507299122388424e-3 | 8.02061461471074e-3 | 1.1312699691660311e-2 | 4.521201121616077e-3 | 8.955788415717926e-2 | 0.10636117567825354 | 6.723987746580207e-2 |
| user_002 | Standing | 1.446034745377e12 | -1.14901 | -9.31124 | -5.99e-4 | -1.2988063636363638 | -9.408781818181819 | 0.27809436363636364 | 1.3202239801000002 | 86.6991903376 | 3.5880100000000004e-7 | 9.381866268312558 | 9.503312295825701 | 0.14979636363636373 | 9.754181818181884e-2 | 0.27869336363636366 | 0.10799272727272728 | 6.397223140495792e-2 | 8.519163636363634e-2 | 2.2438950558677714e-2 | 9.514406294215004e-3 | 7.766999093495043e-2 | 1.3154019740946654e-2 | 4.955787694064486e-3 | 1.3211649912936137e-2 | 0.11469097497600521 | 7.039735573204782e-2 | 0.11494194148758814 |
| user_002 | Standing | 1.446034745417e12 | -1.03405 | -9.31124 | 0.382604 | -1.2570027272727275 | -9.4262 | 0.22235563636363634 | 1.0692594024999997 | 86.6991903376 | 0.146385820816 | 9.376291141006448 | 9.513699984452389 | 0.22295272727272764 | 0.11495999999999995 | 0.16024836363636366 | 0.11147636363636365 | 6.397223140495889e-2 | 0.10799378512396694 | 4.970791859834727e-2 | 1.3215801599999988e-2 | 2.567953804813224e-2 | 1.5727896119008274e-2 | 5.2051216252441725e-3 | 1.6874486719385424e-2 | 0.12541090909090913 | 7.214652885097225e-2 | 0.12990183493463603 |
| user_002 | Standing | 1.44603474549e12 | -1.26397 | -9.65612 | 0.420925 | -1.2291336363636363 | -9.419232727272727 | 0.22932300000000003 | 1.5976201609 | 93.24065345439999 | 0.177177855625 | 9.747586956315136 | 9.503033480331121 | 3.4836363636363776e-2 | 0.23688727272727306 | 0.19160199999999997 | 9.089123966942156e-2 | 6.745586776859498e-2 | 8.645821487603306e-2 | 1.2135722314049683e-3 | 5.611557998016545e-2 | 3.671132640399999e-2 | 1.4727250651840747e-2 | 9.429456238016524e-3 | 1.0043031126861007e-2 | 0.12135588429013547 | 9.71053872759721e-2 | 0.10021492467123351 |
| user_002 | Standing | 1.446034745523e12 | -1.18733 | -9.46452 | 0.267643 | -1.2639699999999998 | -9.433167272727273 | 0.20493736363636364 | 1.4097525289 | 89.5771388304 | 7.163277544900001e-2 | 9.542459019285804 | 9.520695509724966 | 7.663999999999982e-2 | 3.135272727272742e-2 | 6.270563636363638e-2 | 8.329057851239673e-2 | 7.220628099173529e-2 | 7.569057851239669e-2 | 5.873689599999972e-3 | 9.829935074380258e-4 | 3.931996831768597e-3 | 1.132483541397447e-2 | 9.218735968745291e-3 | 8.224858660748308e-3 | 0.10641820997354949 | 9.601424877977899e-2 | 9.069100650421909e-2 |
| user_002 | Standing | 1.446034745539e12 | -1.34061 | -9.34956 | 0.152682 | -1.295322727272727 | -9.433167272727273 | 0.20145363636363636 | 1.7972351721000002 | 87.41427219360001 | 2.3311793124000002e-2 | 9.446418324361039 | 9.524871193110537 | 4.5287272727273065e-2 | 8.360727272727253e-2 | 4.877163636363635e-2 | 9.785851239669428e-2 | 7.632330578512397e-2 | 7.220685123966941e-2 | 2.0509370710744108e-3 | 6.990176052892529e-3 | 2.3786725135867756e-3 | 1.3167258710743819e-2 | 9.747191513147997e-3 | 7.69196991542374e-3 | 0.11474867629190247 | 9.872786594041216e-2 | 8.770387628505219e-2 |
| user_002 | Standing | 1.446034745579e12 | -1.18733 | -9.50284 | 0.382604 | -1.305773636363636 | -9.412265454545455 | 0.2502249090909091 | 1.4097525289 | 90.30396806560002 | 0.146385820816 | 9.584367815109978 | 9.506742054304711 | 0.11844363636363608 | 9.057454545454569e-2 | 0.1323790909090909 | 9.627504132231404e-2 | 6.365553719008267e-2 | 6.523942975206612e-2 | 1.4028894995041256e-2 | 8.203748284297563e-3 | 1.752422370991735e-2 | 1.0408036755522163e-2 | 5.292278176408668e-3 | 7.473513842845228e-3 | 0.10201978609819844 | 7.274804585972511e-2 | 8.644948723298032e-2 |
| user_002 | Standing | 1.446034745774e12 | -1.37893 | -9.57948 | 0.305964 | -1.2953227272727272 | -9.468003636363635 | 0.33731663636363635 | 1.9014479449 | 91.7664370704 | 9.361396929600001e-2 | 9.683052152322428 | 9.563434381356533 | 8.360727272727275e-2 | 0.11147636363636515 | 3.135263636363633e-2 | 7.980694214876033e-2 | 9.944198347107451e-2 | 0.11876137190082643 | 6.990176052892566e-3 | 1.2426979649587114e-2 | 9.829878069504113e-4 | 8.788469450338093e-3 | 1.4384140684598085e-2 | 2.3637476073870768e-2 | 9.374683701511263e-2 | 0.11993390131484127 | 0.1537448408040763 |
| user_002 | Standing | 1.446034745863e12 | -1.26397 | -9.57948 | 0.152682 | -1.2988063636363636 | -9.398330909090909 | 0.29202918181818177 | 1.5976201609 | 91.7664370704 | 2.3311793124000002e-2 | 9.663714038837448 | 9.49352615421479 | 3.4836363636363554e-2 | 0.18114909090909137 | 0.13934718181818176 | 8.614082644628099e-2 | 0.1089428099173556 | 9.880960330578509e-2 | 1.213572231404953e-3 | 3.281499313719025e-2 | 1.9417637080669407e-2 | 1.1974648181517649e-2 | 1.628172635552224e-2 | 1.4143904976522913e-2 | 0.10942873562971314 | 0.127599868164204 | 0.11892815047970313 |
| user_002 | Standing | 1.446034745879e12 | -1.11069 | -9.4262 | 0.267643 | -1.2674536363636362 | -9.40529818181818 | 0.2885454545454545 | 1.2336322760999998 | 88.85324643999999 | 7.163277544900001e-2 | 9.495183594409799 | 9.496256922093867 | 0.15676363636363622 | 2.0901818181819465e-2 | 2.0902454545454474e-2 | 0.10197553719008262 | 9.975867768595072e-2 | 9.817623966942145e-2 | 2.4574837685950368e-2 | 4.368860033058388e-4 | 4.3691260602479043e-4 | 1.4754831838918096e-2 | 1.5080289846431331e-2 | 1.3826169125608563e-2 | 0.12146946875210288 | 0.12280183160861784 | 0.11758473168574465 |
| user_002 | Standing | 1.446034746017e12 | -1.30229 | -9.34956 | 0.267643 | -1.2430681818181815 | -9.429683636363634 | 0.26764336363636365 | 1.6959592440999998 | 87.41427219360001 | 7.163277544900001e-2 | 9.443614997083957 | 9.515928998434747 | 5.9221818181818486e-2 | 8.012363636363418e-2 | 3.6363636363168084e-7 | 9.025785123966944e-2 | 7.188958677685987e-2 | 7.094027272727271e-2 | 3.5072237487603665e-3 | 6.419797104131881e-3 | 1.3223140495527202e-13 | 1.1715385022990226e-2 | 1.0259098345304323e-2 | 7.658885274023284e-3 | 0.10823763219412288 | 0.10128720721445686 | 8.751505741312911e-2 |
| user_002 | Standing | 1.446034746535e12 | -1.30229 | -9.4262 | 0.344284 | -1.2709372727272727 | -9.415749090909088 | 0.28854527272727276 | 1.6959592440999998 | 88.85324643999999 | 0.11853147265599999 | 9.521960783197754 | 9.505747606701117 | 3.13527272727272e-2 | 1.0450909090911509e-2 | 5.573872727272722e-2 | 4.9720991735537255e-2 | 3.135272727272791e-2 | 3.483671900826446e-2 | 9.82993507438012e-4 | 1.0922150082649681e-4 | 3.106805717983465e-3 | 3.112261149812179e-3 | 1.8302875744552786e-3 | 2.07193257337716e-3 | 5.5787643343415926e-2 | 4.2781860343553066e-2 | 4.5518486062007375e-2 |
| user_002 | Standing | 1.446034746599e12 | -1.37893 | -9.4262 | 0.229323 | -1.2883554545454545 | -9.415749090909088 | 0.27461063636363636 | 1.9014479449 | 88.85324643999999 | 5.2589038329e-2 | 9.529285567304036 | 9.507647660752928 | 9.057454545454546e-2 | 1.0450909090911509e-2 | 4.528763636363636e-2 | 5.320462809917363e-2 | 3.0719338842975962e-2 | 2.9136206611570247e-2 | 8.203748284297522e-3 | 1.0922150082649681e-4 | 2.0509700074049586e-3 | 3.609825764688214e-3 | 1.812635614725753e-3 | 1.198152850927874e-3 | 6.008182557719276e-2 | 4.257505859920516e-2 | 3.4614344583248635e-2 |
| user_002 | Standing | 1.446034746729e12 | -1.26397 | -9.38788 | 0.229323 | -1.2848718181818182 | -9.422716363636361 | 0.26764327272727273 | 1.5976201609 | 88.13229089439999 | 5.2589038329e-2 | 9.475362794828966 | 9.513878503272904 | 2.0901818181818133e-2 | 3.483636363636222e-2 | 3.832027272727273e-2 | 4.497057851239671e-2 | 3.040264462810007e-2 | 3.071965289256198e-2 | 4.3688600330578305e-4 | 1.2135722314048601e-3 | 1.4684433018925622e-3 | 2.9500837697971475e-3 | 1.5246880216378935e-3 | 1.2786899747573256e-3 | 5.431467361401657e-2 | 3.904725370160997e-2 | 3.575877479385061e-2 |
| user_002 | Standing | 1.446034746923e12 | -1.26397 | -9.38788 | 0.267643 | -1.2883554545454545 | -9.433167272727273 | 0.27461063636363636 | 1.5976201609 | 88.13229089439999 | 7.163277544900001e-2 | 9.476367649619183 | 9.524899544347562 | 2.4385454545454488e-2 | 4.528727272727373e-2 | 6.9676363636363425e-3 | 4.053685950413224e-2 | 2.438545454545588e-2 | 3.040304958677686e-2 | 5.946503933884269e-4 | 2.050937071074471e-3 | 4.8547956495867476e-5 | 2.3852210584522932e-3 | 8.947337087904548e-4 | 1.2533181617272733e-3 | 4.883872498798769e-2 | 2.9912099705477962e-2 | 3.540223385222002e-2 |
| user_002 | Standing | 1.446034747117e12 | -1.30229 | -9.38788 | 0.267643 | -1.2918390909090909 | -9.412265454545453 | 0.27112699999999995 | 1.6959592440999998 | 88.13229089439999 | 7.163277544900001e-2 | 9.481554878496933 | 9.504525431050798 | 1.0450909090909066e-2 | 2.4385454545454266e-2 | 3.4839999999999316e-3 | 3.103603305785123e-2 | 2.9769256198347967e-2 | 3.166985950413223e-2 | 1.0922150082644576e-4 | 5.946503933884161e-4 | 1.2138255999999524e-5 | 1.5688179209616836e-3 | 1.2532891407964217e-3 | 1.3305458310165293e-3 | 3.960830621172387e-2 | 3.5401823975558404e-2 | 3.647664774916315e-2 |
| user_002 | Standing | 1.446034747635e12 | -1.18733 | -9.4262 | 0.191003 | -1.2465518181818183 | -9.419232727272727 | 0.25719236363636366 | 1.4097525289 | 88.85324643999999 | 3.6482146009e-2 | 9.50260391234471 | 9.505104911598867 | 5.9221818181818264e-2 | 6.967272727273155e-3 | 6.618936363636366e-2 | 3.610314049586778e-2 | 3.2619504132232274e-2 | 4.3070768595041324e-2 | 3.5072237487603405e-3 | 4.854288925620431e-5 | 4.3810318585867794e-3 | 1.8909661860255467e-3 | 1.7133433412472477e-3 | 2.9368811336401204e-3 | 4.348524101376865e-2 | 4.1392551760519036e-2 | 5.4192998935656995e-2 |
| user_002 | Standing | 1.446034748024e12 | -1.26397 | -9.34956 | 0.305964 | -1.22565 | -9.426199999999996 | 0.2711270909090909 | 1.5976201609 | 87.41427219360001 | 9.361396929600001e-2 | 9.43957129978878 | 9.509700619608166 | 3.832000000000013e-2 | 7.663999999999582e-2 | 3.483690909090914e-2 | 3.515305785123967e-2 | 3.641983471074394e-2 | 4.908799173553719e-2 | 1.4684224000000102e-3 | 5.87368959999936e-3 | 1.213610235008268e-3 | 1.6405290073628856e-3 | 2.1656748093162987e-3 | 3.4432827263538705e-3 | 4.0503444388877416e-2 | 4.653681133593382e-2 | 5.86794915311463e-2 |
| user_002 | Standing | 1.446034748348e12 | -1.22565 | -9.4262 | 0.305964 | -1.2361009090909092 | -9.412265454545452 | 0.2780944545454545 | 1.5022179224999999 | 88.85324643999999 | 9.361396929600001e-2 | 9.510472035172386 | 9.497400755305605 | 1.0450909090909288e-2 | 1.3934545454548086e-2 | 2.7869545454545508e-2 | 4.3070413223140515e-2 | 2.8502479338842947e-2 | 3.673701652892562e-2 | 1.0922150082645041e-4 | 1.9417155702486672e-4 | 7.767115638429782e-4 | 2.3366781691960947e-3 | 1.39229832366636e-3 | 1.840255381177311e-3 | 4.8339199095517656e-2 | 3.731351395495149e-2 | 4.289819787796815e-2 |
| user_002 | Standing | 1.446034748412e12 | -1.26397 | -9.4262 | 0.191003 | -1.2361009090909092 | -9.419232727272725 | 0.27461081818181815 | 1.5976201609 | 88.85324643999999 | 3.6482146009e-2 | 9.512483836880302 | 9.50422673863462 | 2.7869090909090843e-2 | 6.967272727274931e-3 | 8.360781818181814e-2 | 4.370380165289258e-2 | 2.3118677685950703e-2 | 4.243754545454546e-2 | 7.766862280991699e-4 | 4.854288925622906e-5 | 6.990267261123961e-3 | 2.367569098722766e-3 | 9.984389722013142e-4 | 2.4360166227340345e-3 | 4.86576725576015e-2 | 3.159808494515631e-2 | 4.935601911351881e-2 |
| user_002 | Standing | 1.446034748672e12 | -1.22565 | -9.38788 | 0.267643 | -1.243068181818182 | -9.412265454545453 | 0.27809445454545456 | 1.5022179224999999 | 88.13229089439999 | 7.163277544900001e-2 | 9.471332619665988 | 9.498236108552103 | 1.7418181818182e-2 | 2.4385454545454266e-2 | 1.0451454545454542e-2 | 3.420297520661165e-2 | 2.9135867768595215e-2 | 2.945303305785125e-2 | 3.03393057851246e-4 | 5.946503933884161e-4 | 1.092329021157024e-4 | 1.838010306836968e-3 | 1.5445464763335565e-3 | 1.2985509630510901e-3 | 4.2872022425317985e-2 | 3.930071852184838e-2 | 3.60354126249595e-2 |
| user_002 | Standing | 1.446034748736e12 | -1.18733 | -9.46452 | 0.267643 | -1.236100909090909 | -9.422716363636363 | 0.2746107272727273 | 1.4097525289 | 89.5771388304 | 7.163277544900001e-2 | 9.542459019285804 | 9.507589537597287 | 4.8770909090908976e-2 | 4.180363636363715e-2 | 6.967727272727264e-3 | 3.5153057851239726e-2 | 2.5968925619835336e-2 | 2.6919471074380173e-2 | 2.3786015735537077e-3 | 1.7475440132232066e-3 | 4.854922334710732e-5 | 1.9207538680691223e-3 | 1.1694423320811784e-3 | 1.1926363256273484e-3 | 4.382640605923696e-2 | 3.4197109996038824e-2 | 3.453456711220438e-2 |
| user_002 | Standing | 1.446034749125e12 | -1.22565 | -9.38788 | 0.305964 | -1.2291336363636363 | -9.440134545454542 | 0.28506172727272727 | 1.5022179224999999 | 88.13229089439999 | 9.361396929600001e-2 | 9.47249295519379 | 9.524155645654458 | 3.4836363636363554e-3 | 5.225454545454333e-2 | 2.090227272727274e-2 | 1.7101487603305866e-2 | 3.451966942148714e-2 | 2.0902264462809936e-2 | 1.213572231404953e-5 | 2.7305375206609353e-3 | 4.3690500516528987e-4 | 7.281433388429767e-4 | 1.693484886551462e-3 | 5.957725275657411e-4 | 2.698413124121243e-2 | 4.1151973057819015e-2 | 2.440845196987595e-2 |
| user_002 | Standing | 1.446034749254e12 | -1.26397 | -9.4262 | 0.305964 | -1.2326172727272728 | -9.44361818181818 | 0.2780943636363636 | 1.5976201609 | 88.85324643999999 | 9.361396929600001e-2 | 9.51548635489516 | 9.527868038899914 | 3.13527272727272e-2 | 1.741818181818111e-2 | 2.786963636363643e-2 | 1.868495867768599e-2 | 3.451966942148714e-2 | 2.248575206611572e-2 | 9.82993507438012e-4 | 3.0339305785121503e-4 | 7.76716631041326e-4 | 8.04267415176559e-4 | 1.6802459167543202e-3 | 7.182353014350122e-4 | 2.835960886853976e-2 | 4.0990802831297656e-2 | 2.6799912340062088e-2 |
| user_002 | Standing | 1.446034750031e12 | -1.22565 | -9.4262 | 0.267643 | -1.236100909090909 | -9.433167272727271 | 0.29551272727272726 | 1.5022179224999999 | 88.85324643999999 | 7.163277544900001e-2 | 9.509316333887993 | 9.518482894683405 | 1.0450909090909066e-2 | 6.967272727271379e-3 | 2.786972727272724e-2 | 2.6602314049586816e-2 | 1.678479338842949e-2 | 1.8051983471074385e-2 | 1.0922150082644576e-4 | 4.8542889256179554e-5 | 7.767216982561965e-4 | 9.708577851239689e-4 | 3.938593514650616e-4 | 4.799283172967692e-4 | 3.1158590871924373e-2 | 1.9845890039629405e-2 | 2.1907266312727592e-2 |
| user_002 | Standing | 1.44603475042e12 | -1.26397 | -9.4262 | 0.267643 | -1.2395845454545453 | -9.419232727272725 | 0.28854545454545455 | 1.5976201609 | 88.85324643999999 | 7.163277544900001e-2 | 9.514331262697814 | 9.50497726070341 | 2.438545454545471e-2 | 6.967272727274931e-3 | 2.090245454545453e-2 | 2.3752066115702505e-2 | 1.836826446280959e-2 | 3.2619942148760336e-2 | 5.946503933884378e-4 | 4.854288925622906e-5 | 4.369126060247927e-4 | 8.108869000751338e-4 | 7.744797331329618e-4 | 1.8678324224004497e-3 | 2.8476075924802804e-2 | 2.782947597661447e-2 | 4.321842688484218e-2 |
| user_002 | Standing | 1.446146629678e12 | -3.90807 | -8.65979 | -1.15021 | -1.483440909090909 | -9.346075454545455 | 0.15964963636363638 | 15.2730111249 | 74.99196284409999 | 1.3229830441 | 9.570159717219978 | 9.50707033039524 | 2.424629090909091 | 0.6862854545454553 | 1.3098596363636363 | 0.24163933884297525 | 8.170793388429733e-2 | 0.15391491735537188 | 5.8788262284826445 | 0.4709877251206622 | 1.7157322669746775 | 0.5351878530848986 | 4.3647811274831035e-2 | 0.1580564214329489 | 0.7315653443711632 | 0.20892058604845773 | 0.39756310371178677 |
| user_002 | Standing | 1.446146630065e12 | -4.06135 | -8.69811 | -0.920286 | -2.936128181818182 | -8.969837272727272 | -0.5614690909090908 | 16.4945638225 | 75.65711757209999 | 0.8469263217960001 | 9.643578574180644 | 9.570473428072871 | 1.125221818181818 | 0.27172727272727215 | 0.35881690909090924 | 1.095136694214876 | 0.28661231404958704 | 0.5466187603305785 | 1.2661241401123966 | 7.383571074380134e-2 | 0.12874957424955383 | 1.9696970255275732 | 0.1340274452798649 | 0.5292324695150383 | 1.4034589504248327 | 0.3660975898307238 | 0.7274836558404858 |
| user_002 | Standing | 1.446146630195e12 | -4.138 | -8.62147 | -0.958607 | -3.462161818181818 | -8.840940909090909 | -0.7879073636363636 | 17.123044 | 74.3297449609 | 0.918927380449 | 9.611020567106753 | 9.603644355393744 | 0.675838181818182 | 0.2194709090909086 | 0.1706996363636364 | 1.2363839669421488 | 0.3154319008264464 | 0.5782884462809919 | 0.456757248003306 | 4.816747993718987e-2 | 2.9138365854677693e-2 | 2.085200669743952 | 0.1406128867029302 | 0.5389169399699444 | 1.4440223923969988 | 0.3749838485894162 | 0.7341096239458684 |
| user_002 | Standing | 1.446146630325e12 | -3.44823 | -8.88971 | -1.30349 | -3.9254899999999995 | -8.712044545454544 | -1.0526659090909092 | 11.8902901329 | 79.02694388409998 | 1.6990861801000001 | 9.62373733001374 | 9.61705984576691 | 0.47725999999999935 | 0.1776654545454548 | 0.25082409090909086 | 1.313975123966942 | 0.351218429752066 | 0.6029907603305785 | 0.2277771075999994 | 3.1565013738843066e-2 | 6.291272458037188e-2 | 2.1214598097888056 | 0.14912668898820444 | 0.5451526018450729 | 1.4565231923278137 | 0.38616924914887313 | 0.7383445007888072 |
| user_002 | Standing | 1.446146630518e12 | -4.7128 | -8.42987 | -0.575403 | -4.01955 | -8.712045454545454 | -0.9307372727272726 | 22.21048384 | 71.06270821689999 | 0.331088612409 | 9.67493052529624 | 9.650377318553973 | 0.6932499999999999 | 0.28217545454545423 | 0.3553342727272726 | 0.9101869421487602 | 0.2672932231404958 | 0.32049702479338843 | 0.4805955624999999 | 7.96229871479337e-2 | 0.12626244537461973 | 1.0125608786333584 | 7.885121891044326e-2 | 0.13817941334442224 | 1.0062608402563216 | 0.28080459203945235 | 0.37172491622760806 |
| user_002 | Standing | 1.446146631619e12 | -4.3296 | -8.46819 | -1.15021 | -3.9533581818181815 | -8.767783636363635 | -0.9446729090909092 | 18.74543616 | 71.7102418761 | 1.3229830441 | 9.580118009722009 | 9.667745515757566 | 0.37624181818181857 | 0.29959363636363534 | 0.20553709090909078 | 0.16341512396694247 | 0.13016214876033044 | 0.1114777520661157 | 0.14155790574876062 | 8.975634694958616e-2 | 4.224549573937185e-2 | 4.188655785792646e-2 | 2.6265430298422265e-2 | 2.473097271745002e-2 | 0.2046620576900527 | 0.16206612939915072 | 0.15726084292489984 |
| user_002 | Standing | 1.446146631879e12 | -3.94639 | -8.69811 | -0.958607 | -3.876719090909091 | -8.739914545454546 | -0.9899609090909091 | 15.5739940321 | 75.65711757209999 | 0.918927380449 | 9.599481183097813 | 9.6173605937047 | 6.967090909090912e-2 | 4.180454545454637e-2 | 3.135390909090907e-2 | 0.22548801652892603 | 0.14568033057851212 | 6.238980165289257e-2 | 4.854035573553722e-3 | 1.7476200206612337e-3 | 9.830676152809904e-4 | 8.207837786934642e-2 | 3.512125830518408e-2 | 7.406333674063107e-3 | 0.2864932422751825 | 0.18740666558365549 | 8.606005852928005e-2 |
| user_002 | Standing | 1.446146632266e12 | -3.98471 | -8.73643 | -1.34181 | -3.7304063636363636 | -8.788685454545453 | -1.017830090909091 | 15.877913784100002 | 76.3252091449 | 1.8004540760999999 | 9.695544183030677 | 9.611242388940958 | 0.2543036363636366 | 5.225545454545255e-2 | 0.323979909090909 | 0.28724404958677713 | 0.16024809917355382 | 0.10704445454545458 | 6.467033946776872e-2 | 2.7306325297518576e-3 | 0.10496298149455367 | 0.11599145349669436 | 5.5931224800450895e-2 | 2.068191954192411e-2 | 0.34057518038855145 | 0.2364978325491608 | 0.14381209803741865 |
| user_002 | Standing | 1.446146635116e12 | -3.7722e-2 | -9.69444 | 0.727487 | 0.6520439090909093 | -9.791982727272726 | 0.9469578181818182 | 1.422949284e-3 | 93.9821669136 | 0.529237335169 | 9.721770785101498 | 9.957926927286705 | 0.6897659090909093 | 9.754272727272628e-2 | 0.21947081818181824 | 0.6029904132231405 | 0.6989499173553722 | 0.8797832975206614 | 0.4757770093440085 | 9.51458364380146e-3 | 4.816744003339672e-2 | 0.46044045582756127 | 0.8738876196320068 | 1.327215956067373 | 0.6785576289657064 | 0.9348195652809193 | 1.1520485910183533 |
| user_002 | Standing | 1.446146637188e12 | -1.37893 | -9.19628 | 1.68549 | -1.3649954545454543 | -9.192796363636363 | 1.6262690909090913 | 1.9014479449 | 84.57156583839999 | 2.8408765400999996 | 9.450602643397932 | 9.43563810836698 | 1.3934545454545644e-2 | 3.4836363636365775e-3 | 5.92209090909086e-2 | 9.310810743801658e-2 | 5.130487603305767e-2 | 5.415537190082635e-2 | 1.9417155702479866e-4 | 1.2135722314051077e-5 | 3.5071160735536612e-3 | 1.434001447044403e-2 | 4.9116854349361425e-3 | 4.615093247933882e-3 | 0.11974979945888857 | 7.008341768875247e-2 | 6.793447760845654e-2 |
| user_002 | Standing | 1.446146637964e12 | -0.612526 | -10.23092 | 1.8771 | -1.1768785454545456 | -9.335624545454543 | 1.675042727272727 | 0.375188100676 | 104.67172404639999 | 3.52350441 | 10.419712882660251 | 9.56237677453755 | 0.5643525454545456 | 0.8952954545454563 | 0.20205727272727292 | 0.20141799999999999 | 0.1941345454545454 | 8.80442148760331e-2 | 0.318493795561025 | 0.8015539509297552 | 4.0827141461983546e-2 | 6.478098758774155e-2 | 0.11027337015116495 | 1.4663061363260725e-2 | 0.25452109458302574 | 0.33207434431338556 | 0.1210911283425038 |
| user_002 | Standing | 1.44614664049e12 | -0.229323 | -9.11964 | 2.22198 | -0.20145363636363636 | -9.16841090909091 | 2.0094781818181815 | 5.2589038329e-2 | 83.1678337296 | 4.937195120399999 | 9.389228822876191 | 9.38902462268342 | 2.7869363636363637e-2 | 4.877090909091031e-2 | 0.21250181818181835 | 5.985570247933885e-2 | 4.940429752066177e-2 | 8.677413223140505e-2 | 7.767014294958678e-4 | 2.378601573553838e-3 | 4.515702273057858e-2 | 4.793678202064615e-3 | 3.322981419083465e-3 | 1.2411526699398967e-2 | 6.923639362405161e-2 | 5.764530699964625e-2 | 0.1114070316425268 |
| user_002 | Standing | 1.446146641202e12 | -7.6042e-2 | -9.00468 | 2.10702 | -0.1666170909090909 | -9.112672727272729 | 2.10702 | 5.782385764e-3 | 81.0842619024 | 4.439533280399999 | 9.248220237892477 | 9.35506650966221 | 9.057509090909091e-2 | 0.10799272727272857 | 0.0 | 7.569043801652892e-2 | 4.655404958677794e-2 | 3.45196694214877e-2 | 8.203847093190083e-3 | 1.1662429143801934e-2 | 0.0 | 7.256156008209616e-3 | 3.9573487218633316e-3 | 1.6846589066867126e-3 | 8.518307348417065e-2 | 6.290746157542308e-2 | 4.1044596558946864e-2 |
| user_002 | Standing | 1.446146641267e12 | 5.99e-4 | -9.15796 | 2.14534 | -0.1387477272727273 | -9.119640000000002 | 2.1139872727272726 | 3.5880100000000004e-7 | 83.86823136159998 | 4.6024837156 | 9.405887275318634 | 9.362966593732287 | 0.1393467272727273 | 3.831999999999702e-2 | 3.135272727272742e-2 | 8.0124173553719e-2 | 4.2437024793389094e-2 | 3.420297520661165e-2 | 1.941751040161984e-2 | 1.4684223999997718e-3 | 9.829935074380258e-4 | 8.275570962658902e-3 | 3.4553711170548715e-3 | 1.6636972045078987e-3 | 9.097016523376718e-2 | 5.8782404825380115e-2 | 4.078844449728254e-2 |
| user_002 | Standing | 1.44614664146e12 | -0.229323 | -9.15796 | 2.18366 | -0.1317803636363636 | -9.123123636363637 | 2.117470909090909 | 5.2589038329e-2 | 83.86823136159998 | 4.768370995600001 | 9.417493902070177 | 9.367023316633059 | 9.754263636363639e-2 | 3.483636363636222e-2 | 6.618909090909098e-2 | 9.215867768595043e-2 | 5.510479338842942e-2 | 3.95867768595041e-2 | 9.5145659087686e-3 | 1.2135722314048601e-3 | 4.3809957553719095e-3 | 9.7318850489136e-3 | 4.691008298121717e-3 | 2.1105124351615354e-3 | 9.865031702388796e-2 | 6.849093588294525e-2 | 4.594031383394693e-2 |
| user_002 | Standing | 1.446146641655e12 | -0.114362 | -9.15796 | 2.14534 | -0.145715 | -9.123123636363639 | 2.141856363636364 | 1.3078667044000002e-2 | 83.86823136159998 | 4.6024837156 | 9.406582468901444 | 9.372853221868157 | 3.1353000000000006e-2 | 3.4836363636360446e-2 | 3.4836363636361334e-3 | 8.234104958677686e-2 | 5.067107438016469e-2 | 4.085355371900827e-2 | 9.830106090000005e-4 | 1.2135722314047363e-3 | 1.2135722314047982e-5 | 8.005276711240421e-3 | 4.351208073328302e-3 | 2.86072072366642e-3 | 8.947221195008213e-2 | 6.596368753585796e-2 | 5.348570578824234e-2 |
| user_002 | Standing | 1.446146641719e12 | -0.305964 | -9.11964 | 2.18366 | -0.16661700000000002 | -9.116156363636366 | 2.1453400000000005 | 9.361396929600001e-2 | 83.1678337296 | 4.768370995600001 | 9.382420726789862 | 9.367299888296584 | 0.139347 | 3.483636363634801e-3 | 3.831999999999969e-2 | 8.582476859504132e-2 | 4.560396694214817e-2 | 3.95867768595041e-2 | 1.9417586409e-2 | 1.21357223140387e-5 | 1.4684223999999761e-3 | 8.842669017115704e-3 | 4.033472798196838e-3 | 2.745982985424494e-3 | 9.403546680436964e-2 | 6.350962760241032e-2 | 5.240212768031937e-2 |
| user_002 | Standing | 1.446146644438e12 | 3.90927 | -8.39155 | 1.95374 | 3.67935 | -8.255688181818181 | 2.242881818181818 | 15.282391932899998 | 70.4181114025 | 3.8170999876000002 | 9.461374282999273 | 9.317298429744554 | 0.2299199999999999 | 0.13586181818181942 | 0.28914181818181794 | 0.3942866363636366 | 0.18970033057851296 | 0.18114909090909084 | 5.286320639999995e-2 | 1.8458433639669758e-2 | 8.360299102148747e-2 | 0.19458152507069662 | 5.631123654605581e-2 | 5.5731725287603305e-2 | 0.4411139592788882 | 0.23729988737050806 | 0.2360756770351476 |
| user_002 | Standing | 1.446146645473e12 | 3.87095 | -8.50651 | 1.14901 | 3.5748409090909092 | -8.520444545454545 | 1.246551818181818 | 14.9842539025 | 72.36071238010001 | 1.3202239801000002 | 9.41621953135652 | 9.325419296856154 | 0.2961090909090909 | 1.3934545454544534e-2 | 9.754181818181795e-2 | 0.2359371900826446 | 7.473983471074258e-2 | 0.2527242975206612 | 8.768059371900824e-2 | 1.941715570247677e-4 | 9.514406294214831e-3 | 7.083179815717508e-2 | 1.0853748738692446e-2 | 9.55806506879039e-2 | 0.2661424396017574 | 0.10418132624752117 | 0.3091612050175505 |
| user_002 | Standing | 1.446146645861e12 | 3.90927 | -8.50651 | 1.30229 | 3.6445127272727276 | -8.499542727272727 | 1.295322727272727 | 15.282391932899998 | 72.36071238010001 | 1.6959592440999998 | 9.45193438176017 | 9.340642711649064 | 0.26475727272727223 | 6.967272727273155e-3 | 6.967272727272933e-3 | 0.16563198347107458 | 3.3569586776859464e-2 | 8.075710743801658e-2 | 7.009641346198321e-2 | 4.854288925620431e-5 | 4.854288925620121e-5 | 5.16384289590534e-2 | 1.921857115552206e-3 | 1.162056965447032e-2 | 0.2272409051184522 | 4.383899081356921e-2 | 0.1077987460709554 |
| user_002 | Standing | 1.44630943939e12 | -2.8357 | -9.15855 | -0.919687 | -0.37917272727272716 | -8.903704545454547 | -0.27227118181818183 | 8.04119449 | 83.8790381025 | 0.8458241779690001 | 9.631513732039684 | 9.485421080286345 | 2.456527272727273 | 0.25484545454545326 | 0.6474158181818181 | 2.6565007438016535 | 0.2743099999999995 | 0.9627083801652891 | 6.034526241652895 | 6.494620570247868e-2 | 0.41914724163203304 | 11.457881099280467 | 0.12104149287625797 | 1.5420141471893885 | 3.384949201875926 | 0.347910179322563 | 1.2417786224562688 |
| user_002 | Standing | 1.446309439527e12 | -2.8351 | -9.00468 | -0.958607 | -2.946582727272727 | -9.032546363636364 | -1.0596334545454547 | 8.03779201 | 81.0842619024 | 0.918927380449 | 9.48899263846532 | 9.561832167208955 | 0.11148272727272701 | 2.786636363636319e-2 | 0.10102645454545467 | 7.189355371900823e-2 | 4.8454214876032804e-2 | 0.1656428760330578 | 1.242839848016523e-2 | 7.765342223140247e-4 | 1.0206344518024818e-2 | 7.050244792336583e-3 | 3.951311355822668e-3 | 3.573299577052291e-2 | 8.396573582323079e-2 | 6.285945717091954e-2 | 0.18903173217881414 |
| user_002 | Standing | 1.446309439584e12 | -2.52854 | -8.88971 | -1.15021 | -2.782848181818182 | -9.011643636363637 | -0.9795095454545456 | 6.3935145316 | 79.02694388409998 | 1.3229830441 | 9.313615917558549 | 9.48453872796895 | 0.2543081818181818 | 0.12193363636363763 | 0.17070045454545435 | 0.1383994214876034 | 3.135173553719009e-2 | 0.17038385950413223 | 6.467265133966943e-2 | 1.4867811676859813e-2 | 2.9138645182024726e-2 | 2.761230893005261e-2 | 1.9517035523666578e-3 | 3.624699435783697e-2 | 0.1661695186550548 | 4.417808905290786e-2 | 0.19038643427995855 |
| user_002 | Standing | 1.446309439633e12 | -2.79678 | -8.96635 | -0.767005 | -2.660918181818182 | -9.008157272727273 | -1.007379090909091 | 7.8219783684 | 80.3954323225 | 0.5882966700250001 | 9.42367801661989 | 9.447923264074356 | 0.13586181818181808 | 4.180727272727225e-2 | 0.2403740909090909 | 0.14536504132231415 | 5.700553719008203e-2 | 0.11654578512396686 | 1.8458433639669394e-2 | 1.747848052892522e-3 | 5.77797035803719e-2 | 2.759430341104435e-2 | 5.646793190157676e-3 | 1.9155199036575494e-2 | 0.16611533165558304 | 7.514514748244011e-2 | 0.13840230863889336 |
| user_002 | Standing | 1.446309439649e12 | -2.60518 | -9.11964 | -0.843646 | -2.6713690909090904 | -9.01164 | -0.972542090909091 | 6.7869628323999995 | 83.1678337296 | 0.711738573316 | 9.521897664610558 | 9.450791674980143 | 6.618909090909053e-2 | 0.10800000000000054 | 0.12889609090909104 | 0.1285791735537191 | 6.935785123966892e-2 | 0.112745214876033 | 4.3809957553718505e-3 | 1.1664000000000117e-2 | 1.661420225164466e-2 | 2.1968360809917382e-2 | 7.086725207663338e-3 | 1.809602623196318e-2 | 0.14821727567971751 | 8.418268947748901e-2 | 0.13452147126746414 |
| user_002 | Standing | 1.446309439729e12 | -3.17999 | -8.92803 | -1.15021 | -2.939613636363636 | -9.077832727272728 | -1.0456992727272727 | 10.1123364001 | 79.7097196809 | 1.3229830441 | 9.546991103227237 | 9.602194036506852 | 0.24037636363636405 | 0.14980272727272848 | 0.10451072727272726 | 0.16405082644628097 | 0.1083146280991735 | 0.1263627685950414 | 5.778079619504152e-2 | 2.2440857098347467e-2 | 1.0922492115074376e-2 | 3.738854821299774e-2 | 1.452220730480845e-2 | 2.2055604422929403e-2 | 0.1933611859008879 | 0.12050812132303967 | 0.14851129392382723 |
| user_002 | Standing | 1.446309439957e12 | -3.02671 | -9.04299 | -0.652044 | -2.970967272727273 | -8.921062727272728 | -0.7182336363636364 | 9.1609734241 | 81.7756681401 | 0.42516137793599995 | 9.55833682928866 | 9.430738612352375 | 5.574272727272689e-2 | 0.12192727272727133 | 6.61896363636364e-2 | 6.46104958677686e-2 | 0.10799347107437995 | 8.170783471074386e-2 | 3.1072516438016102e-3 | 1.4866259834710403e-2 | 4.3810679619504175e-3 | 5.441836181141996e-3 | 1.584399507099913e-2 | 8.609895026525927e-3 | 7.376880222114221e-2 | 0.12587293224120558 | 9.278952002530202e-2 |
| user_002 | Standing | 1.446309440142e12 | -3.21831 | -8.92803 | -1.03525 | -3.1347027272727273 | -8.973322727272729 | -0.944673 | 10.357519256099998 | 79.7097196809 | 1.0717425625 | 9.546673844826794 | 9.552865746279188 | 8.360727272727253e-2 | 4.529272727272904e-2 | 9.057700000000002e-2 | 7.885776859504136e-2 | 5.352545454545421e-2 | 0.1203457107438016 | 6.990176052892529e-3 | 2.0514311438018127e-3 | 8.204192929000003e-3 | 7.311394965965448e-3 | 5.694494253643848e-3 | 2.2253120444415478e-2 | 8.550669544524246e-2 | 7.546187284744428e-2 | 0.14917479828850272 |
| user_002 | Standing | 1.446309440224e12 | -3.17999 | -8.92803 | -0.690364 | -3.2670809090909088 | -8.931515454545456 | -0.6381095454545453 | 10.1123364001 | 79.7097196809 | 0.476602452496 | 9.502560630350958 | 9.533604276420244 | 8.709090909090866e-2 | 3.48545454545679e-3 | 5.225445454545463e-2 | 0.10419239669421514 | 7.220925619834695e-2 | 0.16848326446280987 | 7.584826446280917e-3 | 1.2148393388445398e-5 | 2.7305280198429843e-3 | 1.36173836838468e-2 | 7.198124981217072e-3 | 4.1668352085868506e-2 | 0.11669354602482007 | 8.484176436883589e-2 | 0.20412827360723088 |
| user_002 | Standing | 1.446309440288e12 | -3.33327 | -8.81307 | -0.575403 | -3.176506363636364 | -8.945450000000001 | -0.725200818181818 | 11.1106888929 | 77.6702028249 | 0.331088612409 | 9.43991421201533 | 9.521181094714835 | 0.15676363636363622 | 0.13238000000000127 | 0.14979781818181803 | 9.690842975206605e-2 | 7.379165289256241e-2 | 0.10514337190082647 | 2.4574837685950368e-2 | 1.752446440000034e-2 | 2.2439386332033014e-2 | 1.0961866992036046e-2 | 8.906952255221756e-3 | 1.5196411136766343e-2 | 0.10469893500908234 | 9.437665100660098e-2 | 0.12327372443779876 |
| user_002 | Standing | 1.446309440337e12 | -3.14167 | -8.85139 | -0.613724 | -3.1834736363636362 | -8.928032727272726 | -0.6590113636363637 | 9.8700903889 | 78.34710493210001 | 0.37665714817600005 | 9.412430741799698 | 9.502309342667955 | 4.1803636363636265e-2 | 7.664272727272525e-2 | 4.528736363636365e-2 | 7.093950413223132e-2 | 8.361000000000002e-2 | 0.10260973553719008 | 1.7475440132231322e-3 | 5.874107643801343e-3 | 2.050945305132233e-3 | 6.915155224042065e-3 | 9.448836789556731e-3 | 1.2694180663227639e-2 | 8.315741232170507e-2 | 9.720512738305902e-2 | 0.11266845460565987 |
| user_002 | Standing | 1.446309440539e12 | -3.33327 | -8.92803 | -0.613724 | -3.2148263636363628 | -8.966351818181819 | -0.7321682727272726 | 11.1106888929 | 79.7097196809 | 0.37665714817600005 | 9.54971547858762 | 9.554682103101397 | 0.11844363636363742 | 3.832181818181901e-2 | 0.11844427272727254 | 9.469157024793405e-2 | 5.922479338843009e-2 | 0.10070956198347103 | 1.4028894995041572e-2 | 1.4685617487603941e-3 | 1.4029045741892517e-2 | 1.1516800476033089e-2 | 6.835073060556005e-3 | 1.3614304956487593e-2 | 0.1073163569826757 | 8.26745006671102e-2 | 0.11668035377255073 |
| user_002 | Standing | 1.446309440612e12 | -2.95007 | -9.08132 | -0.728685 | -3.1416690909090907 | -8.952418181818182 | -0.6032727272727274 | 8.702913004900001 | 82.47037294239999 | 0.530981829225 | 9.576234530154585 | 9.508196682362653 | 0.19159909090909055 | 0.12890181818181823 | 0.12541227272727262 | 0.14694685950413236 | 7.66427272727272e-2 | 8.772503305785122e-2 | 3.6710211637189946e-2 | 1.6615678730578523e-2 | 1.5728238150619807e-2 | 2.6310709509616868e-2 | 7.315187253718978e-3 | 1.1768533050048829e-2 | 0.1622057628742483 | 8.552886795532241e-2 | 0.10848286984611363 |
| user_002 | Standing | 1.446309440636e12 | -3.14167 | -9.00468 | -0.422122 | -3.07548 | -8.95590272727273 | -0.5754033636363636 | 9.8700903889 | 81.0842619024 | 0.178186982884 | 9.546336432065655 | 9.487635980915412 | 6.618999999999975e-2 | 4.877727272727128e-2 | 0.15328136363636358 | 0.12509504132231408 | 7.125950413223091e-2 | 7.695739669421485e-2 | 4.381116099999967e-3 | 2.379222334710603e-3 | 2.349517643822312e-2 | 2.2137127736138257e-2 | 6.930217224943577e-3 | 9.248688850788124e-3 | 0.14878550916046313 | 8.324792625010893e-2 | 9.617010372661622e-2 |
| user_002 | Standing | 1.446309440782e12 | -3.25663 | -8.92803 | -0.613724 | -3.1242509090909083 | -8.994224545454546 | -0.49876290909090915 | 10.6056389569 | 79.7097196809 | 0.37665714817600005 | 9.523235573373999 | 9.53533008921694 | 0.1323790909090916 | 6.619454545454673e-2 | 0.1149610909090909 | 8.297471074380178e-2 | 6.746057851239697e-2 | 7.09401570247934e-2 | 1.752422370991754e-2 | 4.381717847934053e-3 | 1.3216052423008263e-2 | 1.0216456340946678e-2 | 7.38261434117205e-3 | 6.405567169906085e-3 | 0.10107648757721391 | 8.592214115798122e-2 | 8.003478724845893e-2 |
| user_002 | Standing | 1.446309440798e12 | -3.29495 | -9.04299 | -0.422122 | -3.1312181818181815 | -8.987256363636364 | -0.509213909090909 | 10.856695502500001 | 81.7756681401 | 0.178186982884 | 9.633823261067436 | 9.531845888451208 | 0.1637318181818186 | 5.57336363636356e-2 | 8.709190909090903e-2 | 8.614181818181832e-2 | 6.492661157024848e-2 | 8.1391173553719e-2 | 2.6808108285124102e-2 | 3.106238222313964e-3 | 7.585000629099162e-3 | 1.0834310055897843e-2 | 6.473374883997011e-3 | 9.539953441567238e-3 | 0.1040879918909854 | 8.045728608396514e-2 | 9.7672685237825e-2 |
| user_002 | Standing | 1.446309440814e12 | -3.21831 | -9.00468 | -0.460442 | -3.1521200000000005 | -8.99074090909091 | -0.5092138181818182 | 10.357519256099998 | 81.0842619024 | 0.21200683536400003 | 9.573598487186727 | 9.541973936352518 | 6.61899999999993e-2 | 1.3939090909090623e-2 | 4.8771818181818194e-2 | 8.36084297520662e-2 | 5.004190082644671e-2 | 8.42415123966942e-2 | 4.381116099999908e-3 | 1.9429825537189285e-4 | 2.378690248760332e-3 | 1.0490114115552231e-2 | 3.3300132089406783e-3 | 9.905141429410215e-3 | 0.1024212581232638 | 5.7706266634921705e-2 | 9.952457701196331e-2 |
| user_002 | Standing | 1.446309440855e12 | -3.21831 | -9.08132 | -0.613724 | -3.1834736363636367 | -9.011640909090909 | -0.5092138181818182 | 10.357519256099998 | 82.47037294239999 | 0.37665714817600005 | 9.65425032546163 | 9.571716158707313 | 3.483636363636311e-2 | 6.967909090909075e-2 | 0.10451018181818184 | 5.858876033057856e-2 | 5.2573884297520544e-2 | 8.392478512396695e-2 | 1.213572231404922e-3 | 4.855175709917333e-3 | 1.0922378103669425e-2 | 5.89913569271227e-3 | 3.160019238467303e-3 | 1.06410155043704e-2 | 7.68058311113959e-2 | 5.6214048408447716e-2 | 0.10315529799467597 |
| user_002 | Standing | 1.446309440912e12 | -3.33327 | -9.04299 | -0.460442 | -3.2426954545454545 | -9.046478181818182 | -0.45347518181818186 | 11.1106888929 | 81.7756681401 | 0.21200683536400003 | 9.648749342187514 | 9.621460120455266 | 9.057454545454569e-2 | 3.4881818181826674e-3 | 6.966818181818157e-3 | 6.270545454545452e-2 | 6.080727272727238e-2 | 6.903978512396694e-2 | 8.203748284297563e-3 | 1.216741239670014e-5 | 4.8536555578512053e-5 | 7.010034507588276e-3 | 4.991539042975131e-3 | 7.12044856405109e-3 | 8.372594883062405e-2 | 7.065082478623395e-2 | 8.438275039397028e-2 |
| user_002 | Standing | 1.44630944092e12 | -3.44823 | -8.96635 | -0.460442 | -3.267080909090909 | -9.03951090909091 | -0.4465078181818182 | 11.8902901329 | 80.3954323225 | 0.21200683536400003 | 9.617573981559175 | 9.622979810284674 | 0.18114909090909093 | 7.316090909090889e-2 | 1.3934181818181846e-2 | 7.79067768595041e-2 | 6.36583471074378e-2 | 6.90397438016529e-2 | 3.2814993137190086e-2 | 5.352518619008235e-3 | 1.9416142294214952e-4 | 9.975563742148757e-3 | 5.319298553718942e-3 | 7.120447412447033e-3 | 9.98777439780693e-2 | 7.293352146797069e-2 | 8.438274357027646e-2 |
| user_002 | Standing | 1.446309441113e12 | -2.98839 | -9.11964 | -0.498763 | -2.977936363636364 | -9.063896363636365 | -0.4778608181818182 | 8.9304747921 | 83.1678337296 | 0.24876453016900002 | 9.609738448671171 | 9.552971422139969 | 1.0453636363636054e-2 | 5.574363636363522e-2 | 2.090218181818182e-2 | 7.157595041322316e-2 | 7.030801652892503e-2 | 8.455815702479341e-2 | 1.0927851322313403e-4 | 3.1073529950411947e-3 | 4.3690120476033066e-4 | 7.955146805184082e-3 | 9.614245033583681e-3 | 1.1269865936254697e-2 | 8.919162968117626e-2 | 9.805225664707407e-2 | 0.10615962479330217 |
| user_002 | Standing | 1.446309441211e12 | -3.52487 | -8.96635 | -0.422122 | -3.207859090909091 | -8.994222727272728 | -0.5092136363636364 | 12.424708516899999 | 80.3954323225 | 0.178186982884 | 9.643564062227409 | 9.563940009077472 | 0.317010909090909 | 2.7872727272727715e-2 | 8.709163636363637e-2 | 0.13174487603305796 | 8.107776859504186e-2 | 5.478852892561983e-2 | 0.10049591648264458 | 7.768889256198594e-4 | 7.584953124495869e-3 | 2.5275422294290008e-2 | 1.0267529344177464e-2 | 4.00596427846281e-3 | 0.15898245907737749 | 0.10132881793536064 | 6.329268740117464e-2 |
| user_002 | Standing | 1.446309441316e12 | -3.17999 | -9.04299 | -0.422122 | -3.308884545454545 | -8.882743636363639 | -0.5231483636363636 | 10.1123364001 | 81.7756681401 | 0.178186982884 | 9.595112897881087 | 9.494601582190445 | 0.12889454545454493 | 0.1602463636363609 | 0.10102636363636364 | 7.727338842975216e-2 | 0.10577685950413208 | 9.469228925619833e-2 | 1.6613803847933747e-2 | 2.5678897058676813e-2 | 1.0206326149586777e-2 | 9.576188153268219e-3 | 1.4951642906536465e-2 | 1.1691285803148755e-2 | 9.785799994516656e-2 | 0.12227691076624592 | 0.10812624937150439 |
| user_002 | Standing | 1.446309441648e12 | -3.17999 | -9.00468 | -0.460442 | -3.284499090909091 | -8.813070909090909 | -0.4047037272727273 | 10.1123364001 | 81.0842619024 | 0.21200683536400003 | 9.560784755335934 | 9.414723208997195 | 0.1045090909090911 | 0.1916090909090915 | 5.573827272727272e-2 | 7.505652892561988e-2 | 9.152553719008241e-2 | 6.365590909090907e-2 | 1.092215008264467e-2 | 3.671404371900849e-2 | 3.106755046619834e-3 | 1.0287782779864774e-2 | 1.1713528051014237e-2 | 7.525338950780617e-3 | 0.10142870786845691 | 0.10822905363632372 | 8.674871152230802e-2 |
| user_002 | Standing | 1.446309441777e12 | -3.10335 | -9.04299 | -0.460442 | -3.207859090909091 | -9.022092727272728 | -0.4430239090909092 | 9.6307812225 | 81.7756681401 | 0.21200683536400003 | 9.57175303682476 | 9.586269963210908 | 0.1045090909090911 | 2.08972727272716e-2 | 1.74180909090908e-2 | 6.048859504132231e-2 | 5.067107438016565e-2 | 7.189003305785123e-2 | 1.092215008264467e-2 | 4.3669600743796933e-4 | 3.0338989091735155e-4 | 5.053976720060111e-3 | 3.651941511194646e-3 | 7.6996686364372655e-3 | 7.109132661626248e-2 | 6.043129579278146e-2 | 8.7747755734476e-2 |
| user_002 | Standing | 1.446309441785e12 | -3.21831 | -9.11964 | -0.345482 | -3.207859090909091 | -9.036028181818182 | -0.4465076363636364 | 10.357519256099998 | 83.1678337296 | 0.119357812324 | 9.677019727065973 | 9.59950948652246 | 1.0450909090908844e-2 | 8.361181818181862e-2 | 0.10102563636363637 | 6.080528925619837e-2 | 5.795586776859549e-2 | 7.315675206611569e-2 | 1.0922150082644113e-4 | 6.990936139669494e-3 | 1.0206179202677688e-2 | 5.059492957475588e-3 | 4.286380789932444e-3 | 7.937960476740046e-3 | 7.113011287405348e-2 | 6.547045738294827e-2 | 8.909523262633105e-2 |
| user_002 | Standing | 1.446309442012e12 | -3.25663 | -9.11964 | -5.99e-4 | -3.225277272727273 | -8.99074090909091 | -0.19568399999999997 | 10.6056389569 | 83.1678337296 | 3.5880100000000004e-7 | 9.683670432501357 | 9.554488387265682 | 3.135272727272698e-2 | 0.12889909090909057 | 0.19508499999999998 | 6.71391735537188e-2 | 7.632859504132274e-2 | 7.505711570247933e-2 | 9.82993507437998e-4 | 1.6614975637189996e-2 | 3.805815722499999e-2 | 6.7607005764086965e-3 | 7.2988205059354334e-3 | 8.12994517393163e-3 | 8.222347947155177e-2 | 8.543313470741568e-2 | 9.016620860350973e-2 |
| user_002 | Standing | 1.446309442052e12 | -2.91174 | -9.04299 | -0.1922 | -3.148636363636364 | -9.053447272727272 | -0.12949436363636363 | 8.4782298276 | 81.7756681401 | 3.694084e-2 | 9.502149167830401 | 9.587939103713845 | 0.2368963636363639 | 1.0457272727272482e-2 | 6.270563636363638e-2 | 0.11464413223140472 | 9.184619834710778e-2 | 8.772497520661156e-2 | 5.6119887104132356e-2 | 1.0935455289255685e-4 | 3.931996831768597e-3 | 1.8239278947257662e-2 | 1.1962228035086397e-2 | 1.0954306810793384e-2 | 0.13505287463529853 | 0.109371970975595 | 0.10466282439717259 |
| user_002 | Standing | 1.446309442675e12 | -2.4519 | -9.2346 | -5.99e-4 | -2.7898163636363633 | -9.091769090909091 | -7.375563636363637e-2 | 6.011813610000001 | 85.27783716 | 3.5880100000000004e-7 | 9.554561796796387 | 9.514107151379342 | 0.3379163636363631 | 0.14283090909090923 | 7.315663636363637e-2 | 0.1979376859504133 | 8.139264462809916e-2 | 7.410709917355372e-2 | 0.1141874688132228 | 2.0400668591735577e-2 | 5.351893444041323e-3 | 5.300299064087155e-2 | 9.571282286476414e-3 | 9.1217999584861e-3 | 0.23022378382971545 | 9.783293048087854e-2 | 9.550811462114672e-2 |
| user_002 | Standing | 1.44630944274e12 | -2.41358 | -9.11964 | -5.99e-4 | -2.737560909090909 | -9.10222 | -7.375563636363637e-2 | 5.8253684164 | 83.1678337296 | 3.5880100000000004e-7 | 9.433620858652366 | 9.509199078564167 | 0.32398090909090893 | 1.7419999999999547e-2 | 7.315663636363637e-2 | 0.22264024793388434 | 7.537578512396675e-2 | 7.569057024793388e-2 | 0.1049636294553718 | 3.034563999999842e-4 | 5.351893444041323e-3 | 6.229692536341098e-2 | 8.963426320135297e-3 | 9.325901606570248e-3 | 0.24959352027528875 | 9.4675373356197e-2 | 9.657070780816639e-2 |
| user_002 | Standing | 1.446309444165e12 | -2.14534 | -9.2346 | 3.7722e-2 | -2.1592745454545454 | -9.21718181818182 | 2.0303363636363633e-2 | 4.6024837156 | 85.27783716 | 1.422949284e-3 | 9.480598284121314 | 9.467389115592873 | 1.3934545454545422e-2 | 1.741818181818111e-2 | 1.7418636363636365e-2 | 8.329057851239673e-2 | 3.6103140495868206e-2 | 3.8637115702479345e-2 | 1.9417155702479248e-4 | 3.0339305785121503e-4 | 3.034088927685951e-4 | 9.548606966190842e-3 | 1.9395090752817494e-3 | 2.5838574284244935e-3 | 9.771697378751985e-2 | 4.4039857802696744e-2 | 5.0831657738308056e-2 |
| user_002 | Standing | 1.446309444812e12 | -2.33694 | -9.2346 | 0.229323 | -2.2045618181818183 | -9.213698181818183 | 0.12481318181818181 | 5.461288563599999 | 85.27783716 | 5.2589038329e-2 | 9.528468647265887 | 9.475980846384282 | 0.1323781818181815 | 2.090181818181769e-2 | 0.10450981818181819 | 0.10704264462809908 | 4.3387107438016284e-2 | 7.157342975206611e-2 | 1.752398302148752e-2 | 4.368860033057645e-4 | 1.0922302096396696e-2 | 1.5613158380766323e-2 | 4.877457122764723e-3 | 7.206518220723518e-3 | 0.12495262454533047 | 6.983879382381059e-2 | 8.489121403728138e-2 |
| user_002 | Standing | 1.446309445137e12 | -2.10702 | -9.27292 | 0.191003 | -2.215012727272727 | -9.20673090909091 | 0.1770680909090909 | 4.439533280399999 | 85.98704532639998 | 3.6482146009e-2 | 9.511207113337875 | 9.47215065923818 | 0.10799272727272724 | 6.618909090908964e-2 | 1.3934909090909109e-2 | 9.374148760330576e-2 | 4.465388429752066e-2 | 6.2389198347107454e-2 | 1.1662429143801646e-2 | 4.380995755371733e-3 | 1.9418169137190133e-4 | 1.3790593538692707e-2 | 4.612677726821851e-3 | 5.991821436857253e-3 | 0.11743335786177923 | 6.791669696637087e-2 | 7.74068565235487e-2 |
| user_002 | Standing | 1.446309445202e12 | -2.10702 | -9.31124 | 0.191003 | -2.2219799999999994 | -9.213698181818183 | 0.18403545454545453 | 4.439533280399999 | 86.6991903376 | 3.6482146009e-2 | 9.548570875477074 | 9.48057858207769 | 0.11495999999999951 | 9.754181818181706e-2 | 6.967545454545476e-3 | 9.405818181818174e-2 | 5.1937851239669215e-2 | 5.922227272727274e-2 | 1.3215801599999886e-2 | 9.514406294214657e-3 | 4.8546689661157324e-5 | 1.3862304625093896e-2 | 5.4500425664912484e-3 | 5.837366443745307e-3 | 0.11773828869613273 | 7.382440359726077e-2 | 7.640265992585145e-2 |
| user_002 | Standing | 1.446309446237e12 | -1.8771 | -9.27292 | 0.229323 | -1.8143936363636362 | -9.283370909090909 | 0.16661681818181817 | 3.52350441 | 85.98704532639998 | 5.2589038329e-2 | 9.463780363825494 | 9.46103804727223 | 6.270636363636384e-2 | 1.0450909090909732e-2 | 6.270618181818183e-2 | 9.532586776859513e-2 | 4.4970578512396386e-2 | 5.700548760330579e-2 | 3.932088040495893e-3 | 1.092215008264597e-4 | 3.932065238214877e-3 | 1.5334474965364404e-2 | 3.1883852261457065e-3 | 5.759045718879038e-3 | 0.12383244714275982 | 5.646578810346763e-2 | 7.58883767047302e-2 |
| user_002 | Standing | 1.446309446367e12 | -1.8771 | -9.31124 | 0.191003 | -1.8387799999999999 | -9.269436363636363 | 0.17010054545454545 | 3.52350441 | 86.6991903376 | 3.6482146009e-2 | 9.500482982123014 | 9.451956712146142 | 3.832000000000013e-2 | 4.180363636363715e-2 | 2.0902454545454557e-2 | 5.9538677685950496e-2 | 3.546975206611546e-2 | 4.3704363636363645e-2 | 1.4684224000000102e-3 | 1.7475440132232066e-3 | 4.369126060247939e-4 | 4.717472994891065e-3 | 1.7188595786626333e-3 | 4.304953559382419e-3 | 6.868386269635005e-2 | 4.1459131426775375e-2 | 6.561214490764967e-2 |
| user_002 | Standing | 1.446309446755e12 | -1.8771 | -9.31124 | 0.267643 | -1.8771000000000002 | -9.27292 | 0.187519 | 3.52350441 | 86.6991903376 | 7.163277544900001e-2 | 9.502332741124624 | 9.463018714575734 | 2.220446049250313e-16 | 3.8320000000000576e-2 | 8.012400000000003e-2 | 3.768694214876043e-2 | 2.438545454545443e-2 | 4.1804132231404965e-2 | 4.930380657631324e-32 | 1.4684224000000442e-3 | 6.419855376000005e-3 | 1.8214955675432069e-3 | 9.278311332832246e-4 | 2.762579202105185e-3 | 4.267898273791453e-2 | 3.0460320636579396e-2 | 5.256024355066465e-2 |
| user_002 | Standing | 1.446309447209e12 | -1.76214 | -9.19628 | 0.152682 | -1.8178772727272723 | -9.276403636363636 | 0.17010054545454545 | 3.1051373796000004 | 84.57156583839999 | 2.3311793124000002e-2 | 9.364828616217382 | 9.454909867659602 | 5.573727272727225e-2 | 8.012363636363595e-2 | 1.7418545454545437e-2 | 5.5422396694214934e-2 | 2.311867768595054e-2 | 6.270592561983471e-2 | 3.106643571074327e-3 | 6.419797104132165e-3 | 3.034057257520655e-4 | 5.146896537866269e-3 | 1.0602208312546976e-3 | 4.907304573876034e-3 | 7.174187436822563e-2 | 3.256103240461975e-2 | 7.005215609726823e-2 |
| user_002 | Standing | 1.446309448892e12 | -1.64717 | -9.34956 | 0.229323 | -1.6820099999999998 | -9.297305454545453 | 0.20493727272727275 | 2.7131690089 | 87.41427219360001 | 5.2589038329e-2 | 9.496316667046704 | 9.450607340367052 | 3.483999999999976e-2 | 5.2254545454546886e-2 | 2.4385727272727253e-2 | 3.38911570247934e-2 | 2.7869090909091652e-2 | 3.7686900826446275e-2 | 1.2138255999999833e-3 | 2.7305375206613065e-3 | 5.946636946198338e-4 | 1.5261275652141198e-3 | 1.0988344931630656e-3 | 1.8148731459241166e-3 | 3.9065682705081706e-2 | 3.3148672570150764e-2 | 4.260132798310537e-2 |
| user_002 | Standing | 1.446309449087e12 | -1.68549 | -9.31124 | 0.229323 | -1.671558181818182 | -9.304272727272727 | 0.19100263636363637 | 2.8408765400999996 | 86.6991903376 | 5.2589038329e-2 | 9.465339714771414 | 9.455417417042819 | 1.393181818181799e-2 | 6.967272727273155e-3 | 3.8320363636363625e-2 | 3.420685950413225e-2 | 3.040264462810007e-2 | 4.4020801652892565e-2 | 1.940955578512343e-4 | 4.854288925620431e-5 | 1.4684502692231395e-3 | 1.5337898335837702e-3 | 1.244463160931684e-3 | 2.810012106846732e-3 | 3.916362896341158e-2 | 3.5276949427801775e-2 | 5.3009547317881635e-2 |
| user_002 | Standing | 1.446309449281e12 | -1.68549 | -9.31124 | 7.6042e-2 | -1.6750409090909093 | -9.304272727272725 | 0.17706799999999998 | 2.8408765400999996 | 86.6991903376 | 5.782385764e-3 | 9.462866862820379 | 9.455808171509537 | 1.044909090909063e-2 | 6.967272727274931e-3 | 0.10102599999999998 | 3.51559504132231e-2 | 2.3118677685951508e-2 | 4.813788429752066e-2 | 1.0918350082644046e-4 | 4.854288925622906e-5 | 1.0206252675999995e-2 | 1.921045821712995e-3 | 7.402790611570776e-4 | 3.2788972391074376e-3 | 4.382973672876664e-2 | 2.7208069780068515e-2 | 5.726165592355357e-2 |
| user_002 | Standing | 1.446309449346e12 | -1.72382 | -9.2346 | 0.191003 | -1.6750409090909093 | -9.30078909090909 | 0.17706799999999998 | 2.9715553923999996 | 85.27783716 | 3.6482146009e-2 | 9.39605633754976 | 9.45238415484924 | 4.8779090909090606e-2 | 6.618909090908964e-2 | 1.393500000000003e-2 | 3.768991735537181e-2 | 2.6602314049587763e-2 | 4.7821247933884294e-2 | 2.379399709917326e-3 | 4.380995755371733e-3 | 1.9418422500000086e-4 | 2.0976241562734722e-3 | 1.0679435636364012e-3 | 3.2689697391983467e-3 | 4.579982703322658e-2 | 3.2679405802988545e-2 | 5.7174904802704714e-2 |
| user_002 | Standing | 1.446309450511e12 | -1.68549 | -9.27292 | 0.229323 | -1.7238163636363637 | -9.321690909090908 | 0.17358436363636365 | 2.8408765400999996 | 85.98704532639998 | 5.2589038329e-2 | 9.427646095650228 | 9.481651493435997 | 3.832636363636377e-2 | 4.877090909090853e-2 | 5.573863636363635e-2 | 3.610776859504142e-2 | 2.5652231404959282e-2 | 5.47885867768595e-2 | 1.468910149586787e-3 | 2.3786015735536644e-3 | 3.1067955836776846e-3 | 2.1870113903831815e-3 | 9.035596886551832e-4 | 4.149367334275732e-3 | 4.6765493586438084e-2 | 3.0059269596169218e-2 | 6.441558300811794e-2 |
| user_002 | Standing | 1.446309450576e12 | -1.76214 | -9.31124 | 0.229323 | -1.7238163636363633 | -9.325174545454544 | 0.17358436363636365 | 3.1051373796000004 | 86.6991903376 | 5.2589038329e-2 | 9.479288831738856 | 9.485073123464772 | 3.832363636363678e-2 | 1.3934545454544534e-2 | 5.573863636363635e-2 | 3.389066115702488e-2 | 2.4702148760331125e-2 | 5.3521801652892546e-2 | 1.4687011041322633e-3 | 1.941715570247677e-4 | 3.1067955836776846e-3 | 1.9630049326070705e-3 | 8.671525217130334e-4 | 3.990497393471074e-3 | 4.430581149924997e-2 | 2.9447453569248282e-2 | 6.317038383191188e-2 |
| user_002 | Standing | 1.446309451548e12 | -1.80046 | -9.27292 | 0.344284 | -1.7203309090909094 | -9.30078909090909 | 0.21190472727272727 | 3.2416562115999996 | 85.98704532639998 | 0.11853147265599999 | 9.452366529639866 | 9.461414500506855 | 8.01290909090906e-2 | 2.7869090909090843e-2 | 0.1323792727272727 | 6.397702479338839e-2 | 2.5968925619835014e-2 | 4.9721561983471066e-2 | 6.420671209917305e-3 | 7.766862280991699e-4 | 1.7524271847801646e-2 | 4.660743295416974e-3 | 1.0458786139744642e-3 | 4.087612653542448e-3 | 6.826963670195539e-2 | 3.234004659821108e-2 | 6.39344402770717e-2 |
| user_002 | Standing | 1.446309452195e12 | -1.76214 | -9.27292 | 0.191003 | -1.7377509090909091 | -9.293821818181817 | 0.21538845454545452 | 3.1051373796000004 | 85.98704532639998 | 3.6482146009e-2 | 9.440797892763566 | 9.457747834483266 | 2.4389090909090916e-2 | 2.090181818181769e-2 | 2.4385454545454516e-2 | 6.144355371900834e-2 | 1.868495867768629e-2 | 4.243735537190081e-2 | 5.948277553719011e-4 | 4.368860033057645e-4 | 5.946503933884283e-4 | 4.3540243727272785e-3 | 4.02685331329835e-4 | 3.2943620511735523e-3 | 6.598503142931189e-2 | 2.0067020987925312e-2 | 5.739653344213004e-2 |
| user_002 | Standing | 1.446309452454e12 | -1.80046 | -9.27292 | 0.267643 | -1.7412354545454547 | -9.293821818181817 | 0.22235572727272726 | 3.2416562115999996 | 85.98704532639998 | 7.163277544900001e-2 | 9.449885412715277 | 9.458418439308344 | 5.922454545454525e-2 | 2.090181818181769e-2 | 4.528727272727276e-2 | 4.212388429752072e-2 | 2.1535206611570438e-2 | 4.180398347107437e-2 | 3.5075467842974966e-3 | 4.368860033057645e-4 | 2.050937071074383e-3 | 2.4330252048835464e-3 | 5.78101681142011e-4 | 2.6809481589226154e-3 | 4.932570531562166e-2 | 2.404374515631895e-2 | 5.177787325607933e-2 |
| user_002 | Standing | 1.446309453167e12 | -1.72382 | -9.31124 | 0.152682 | -1.7377527272727271 | -9.297305454545453 | 0.21887199999999998 | 2.9715553923999996 | 86.6991903376 | 2.3311793124000002e-2 | 9.470694669512053 | 9.461044992500538 | 1.3932727272727208e-2 | 1.393454545454631e-2 | 6.618999999999997e-2 | 3.578809917355381e-2 | 2.438545454545491e-2 | 3.166970247933884e-2 | 1.9412088925619653e-4 | 1.9417155702481723e-4 | 4.381116099999996e-3 | 1.9916283206611635e-3 | 7.954414353118044e-4 | 1.7872984655176556e-3 | 4.462766317723978e-2 | 2.8203571321940853e-2 | 4.227645284928308e-2 |
| user_002 | Standing | 1.446309453426e12 | -1.83878 | -9.34956 | 0.229323 | -1.7482045454545452 | -9.304272727272725 | 0.22583927272727272 | 3.3811118884000004 | 87.41427219360001 | 5.2589038329e-2 | 9.53142030970878 | 9.469913612038733 | 9.05754545454549e-2 | 4.528727272727551e-2 | 3.4837272727272772e-3 | 3.1353471074380294e-2 | 2.0585123966942766e-2 | 2.026857024793389e-2 | 8.203912966115768e-3 | 2.050937071074632e-3 | 1.2136355710743833e-5 | 1.5700346107438133e-3 | 6.453997776108385e-4 | 9.311543497137493e-4 | 3.9623662258097915e-2 | 2.5404719593233822e-2 | 3.051482180373579e-2 |
| user_002 | Standing | 1.446309453944e12 | -1.60885 | -9.27292 | 0.229323 | -1.7238163636363637 | -9.297305454545453 | 0.22235572727272723 | 2.5883983225 | 85.98704532639998 | 5.2589038329e-2 | 9.41424626229997 | 9.458597818253317 | 0.11496636363636381 | 2.4385454545454266e-2 | 6.967272727272766e-3 | 5.035743801652901e-2 | 2.7552396694215275e-2 | 1.646814049586778e-2 | 1.3217264767768635e-2 | 5.946503933884161e-4 | 4.854288925619889e-5 | 3.6488926099924987e-3 | 1.609638077836221e-3 | 4.986680926889558e-4 | 6.04060643478161e-2 | 4.0120295086604495e-2 | 2.2330877561998225e-2 |
| user_002 | Standing | 1.446309454396e12 | -1.76214 | -9.19628 | 0.229323 | -1.6959445454545452 | -9.286854545454544 | 0.21190463636363638 | 3.1051373796000004 | 84.57156583839999 | 5.2589038329e-2 | 9.366391634793464 | 9.443339941172797 | 6.619545454545483e-2 | 9.057454545454391e-2 | 1.741836363636362e-2 | 7.569520661157031e-2 | 3.4519669421487625e-2 | 4.528764462809917e-2 | 4.381838202479377e-3 | 8.20374828429724e-3 | 3.033993917685945e-4 | 6.846485584673188e-3 | 1.8104291197595676e-3 | 3.262361712950412e-3 | 8.274349270288986e-2 | 4.254913770876641e-2 | 5.711708774920524e-2 |
| user_002 | Standing | 1.446309454979e12 | -1.76214 | -9.2346 | 0.267643 | -1.7238172727272725 | -9.283370909090907 | 0.1979698181818182 | 3.1051373796000004 | 85.27783716 | 7.163277544900001e-2 | 9.405030957686902 | 9.444498128845453 | 3.8322727272727564e-2 | 4.8770909090906756e-2 | 6.967318181818183e-2 | 4.97257024793389e-2 | 3.705322314049604e-2 | 5.447190082644627e-2 | 1.468631425619857e-3 | 2.3786015735534913e-3 | 4.854352264669423e-3 | 3.2517609446281095e-3 | 1.8810369586776802e-3 | 3.4708763509466558e-3 | 5.70242136695291e-2 | 4.3370922963175226e-2 | 5.8914143895559205e-2 |
| user_002 | Standing | 1.446309455045e12 | -1.68549 | -9.27292 | 0.152682 | -1.7273009090909093 | -9.276403636363636 | 0.1979698181818182 | 2.8408765400999996 | 85.98704532639998 | 2.3311793124000002e-2 | 9.426093234189018 | 9.438254328626359 | 4.181090909090934e-2 | 3.4836363636365775e-3 | 4.528781818181818e-2 | 4.909264462809928e-2 | 3.1669421487603315e-2 | 5.3521793388429745e-2 | 1.7481521190082854e-3 | 1.2135722314051077e-5 | 2.0509864756694213e-3 | 3.194415111419999e-3 | 1.5246880216378377e-3 | 3.374890019552966e-3 | 5.651915703033794e-2 | 3.9047253701609254e-2 | 5.809380362442251e-2 |
| user_002 | Standing | 1.446309455304e12 | -1.76214 | -9.38788 | 0.191003 | -1.7342681818181818 | -9.29033818181818 | 0.20145354545454544 | 3.1051373796000004 | 88.13229089439999 | 3.6482146009e-2 | 9.553738033880194 | 9.453277670470474 | 2.7871818181818275e-2 | 9.754181818181884e-2 | 1.0450545454545435e-2 | 4.4974132231404965e-2 | 3.0402644628099426e-2 | 4.718776859504132e-2 | 7.768382487603358e-4 | 9.514406294215004e-3 | 1.0921390029752025e-4 | 2.8908970950413254e-3 | 1.5908828706236021e-3 | 2.904897033332833e-3 | 5.376706329195714e-2 | 3.988587307084555e-2 | 5.38970967059714e-2 |
| user_002 | Standing | 1.446309455951e12 | -1.80046 | -9.27292 | 0.191003 | -1.7307836363636364 | -9.290338181818182 | 0.2049374545454545 | 3.2416562115999996 | 85.98704532639998 | 3.6482146009e-2 | 9.44802538544478 | 9.45255363763839 | 6.967636363636354e-2 | 1.7418181818182887e-2 | 1.39344545454545e-2 | 3.5156363636363756e-2 | 3.1669421487603634e-2 | 2.7552429752066107e-2 | 4.854795649586763e-3 | 3.0339305785127694e-4 | 1.9416902347933756e-4 | 1.5879042399699554e-3 | 1.6791426692712252e-3 | 1.3845920594936134e-3 | 3.984851615769344e-2 | 4.097734336522105e-2 | 3.721010695353634e-2 |
| user_002 | Walking | 1.446034819939e12 | -3.33327 | -8.27659 | 2.41358 | -3.33327 | -8.27659 | 2.41358 | 11.1106888929 | 68.5019420281 | 5.8253684164 | 9.243267784577053 | 9.243267784577053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_002 | Walking | 1.446034820263e12 | -4.59784 | -10.49917 | 1.91542 | -4.514810000000001 | -8.857781666666666 | 2.426355 | 21.140132665599996 | 110.23257068889998 | 3.6688337763999996 | 11.62073737466345 | 10.281695257314567 | 8.302999999999905e-2 | 1.6413883333333334 | 0.5109350000000001 | 0.5147690555555552 | 0.549363861111111 | 0.35712352777777784 | 6.893980899999842e-3 | 2.694155660802778 | 0.26105457422500017 | 0.5914448370375734 | 0.5973346555665787 | 0.1603337250907825 | 0.769054508495707 | 0.7728742818638609 | 0.40041693906574743 |
| user_002 | Walking | 1.44603482188e12 | -3.37159 | -7.12698 | 0.344284 | -4.441075454545454 | -8.50651090909091 | 1.7551674545454545 | 11.3676191281 | 50.79384392039999 | 0.11853147265599999 | 7.891767515655539 | 9.827429664775083 | 1.0694854545454544 | 1.37953090909091 | 1.4108834545454545 | 1.2243503305785126 | 0.9586395041322315 | 0.7787576694214876 | 1.1437991374842973 | 1.9031055291371928 | 1.9905921223101155 | 1.9586936848807666 | 1.271840119460106 | 0.7567245327015779 | 1.3995333811241397 | 1.12775889243229 | 0.869899150879904 |
| user_002 | Walking | 1.446034822981e12 | -5.01936 | -7.39522 | 1.37893 | -4.69189909090909 | -8.649341818181817 | 2.0408281818181817 | 25.193974809599997 | 54.6892788484 | 1.9014479449 | 9.043489459434339 | 10.113432651366857 | 0.3274609090909095 | 1.254121818181817 | 0.6618981818181817 | 1.132189256198347 | 0.6324439669421487 | 0.8351294297520663 | 0.10723064698264491 | 1.5728215348396666 | 0.43810920309421475 | 1.8307273614960182 | 0.8123552954916602 | 0.8564678106434943 | 1.353043739683244 | 0.9013075476726355 | 0.9254554611884325 |
| user_002 | Walking | 1.446034823435e12 | -1.99206 | -9.8094 | 2.37526 | -5.482690000000001 | -8.321878181818182 | 0.8528983636363637 | 3.9683030435999997 | 96.22432836 | 5.6418600676 | 10.287589196269455 | 10.29735939154132 | 3.490630000000001 | 1.4875218181818184 | 1.522361636363636 | 1.561314132231405 | 1.4428704132231402 | 1.0606175289256201 | 12.184497796900006 | 2.212721159566943 | 2.3175849518717677 | 4.833273639667995 | 2.4727328289848227 | 2.0153629870589587 | 2.19847075024163 | 1.5724925529187166 | 1.4196348076385557 |
| user_002 | Walking | 1.446034823888e12 | -4.02303 | -8.81307 | 2.49022 | -4.392302727272727 | -8.461224545454545 | 1.8945141818181819 | 16.1847703809 | 77.6702028249 | 6.2011956484 | 10.002808048453195 | 10.078300610597479 | 0.369272727272727 | 0.3518454545454546 | 0.595705818181818 | 2.3115707438016537 | 0.7854072727272722 | 1.0504829008264465 | 0.1363623471074378 | 0.12379522388429753 | 0.3548654218156692 | 6.871309212190386 | 1.1250577196958667 | 1.3297210894746243 | 2.621318220321674 | 1.0606873807563975 | 1.153135330078228 |
| user_002 | Walking | 1.446034826609e12 | -4.55952 | -11.18893 | 1.18733 | -3.873236363636363 | -8.973320909090909 | 1.9641890909090909 | 20.7892226304 | 125.19215454489998 | 1.4097525289 | 12.140474854971693 | 10.02710253131051 | 0.6862836363636369 | 2.2156090909090906 | 0.7768590909090909 | 1.2693209917355377 | 0.8946678512396692 | 0.9998124380165287 | 0.47098522954049654 | 4.908923643719007 | 0.6035100471280992 | 2.044263508774306 | 1.50521872836281 | 1.3470822206397992 | 1.4297774333001294 | 1.2268735584251582 | 1.1606387123647905 |
| user_002 | Walking | 1.446034826673e12 | -5.05768 | -8.58315 | 0.191003 | -4.124059999999999 | -9.074347272727273 | 1.838777545454545 | 25.580126982400003 | 73.67046392249999 | 3.6482146009e-2 | 9.96428989195462 | 10.208070843567992 | 0.9336200000000012 | 0.4911972727272733 | 1.647774545454545 | 1.116356280991736 | 0.8408293388429752 | 1.0527011983471075 | 0.8716463044000022 | 0.24127476073471132 | 2.715160952647932 | 1.5012614522227659 | 1.4204436278609316 | 1.490610296004187 | 1.225259748878892 | 1.1918236563606763 | 1.2209055229640773 |
| user_002 | Walking | 1.446034827062e12 | -7.58682 | -12.33854 | 0.919089 | -5.524493636363636 | -8.743399090909092 | 0.3303500909090911 | 57.559837712400004 | 152.2395693316 | 0.8447245899210001 | 14.513584382705774 | 10.723474058446866 | 2.0623263636363642 | 3.595140909090908 | 0.588738909090909 | 1.9058815702479337 | 2.0046887603305787 | 1.2332184545454543 | 4.2531900301495895 | 12.925038156219001 | 0.34661350307755356 | 6.280181072242599 | 4.928729884761232 | 2.9157007997075812 | 2.506028944813407 | 2.2200742971263896 | 1.707542327354605 |
| user_002 | Walking | 1.446034827321e12 | -3.79311 | -7.47186 | 2.29862 | -5.01936090909091 | -7.7157181818181835 | 0.45227736363636367 | 14.387683472099999 | 55.828691859600006 | 5.2836539044 | 8.68907528083973 | 9.596399504089618 | 1.22625090909091 | 0.24385818181818308 | 1.8463426363636364 | 2.1399206611570247 | 1.470106280991736 | 1.7123802561983468 | 1.503691292046283 | 5.9466812839670036e-2 | 3.4089811308542233 | 6.922151212780466 | 3.67483293401142 | 3.8169152639420676 | 2.630998140018435 | 1.9169853765773541 | 1.9536927250573637 |
| user_002 | Walking | 1.446034827515e12 | -4.5212 | -7.81675 | 2.4519 | -5.2214136363636365 | -7.9526081818181815 | 0.8912193636363636 | 20.441249440000004 | 61.1015805625 | 6.011813610000001 | 9.357063835012562 | 9.956367328994729 | 0.7002136363636362 | 0.13585818181818166 | 1.5606806363636365 | 2.0373108264462814 | 1.0853197520661162 | 1.7902876528925618 | 0.49029913654958657 | 1.8457445566942104e-2 | 2.4357240487204055 | 6.667947702660329 | 2.6194819470658905 | 3.9902978286812867 | 2.5822369571091515 | 1.6184813706267647 | 1.9975729845693466 |
| user_002 | Walking | 1.446034827839e12 | -4.3296 | -10.30757 | -0.728685 | -4.2459881818181815 | -8.886230909090909 | 1.7238145454545455 | 18.74543616 | 106.2459993049 | 0.530981829225 | 11.203678739330444 | 10.121340584469245 | 8.361181818181862e-2 | 1.4213390909090915 | 2.4524995454545455 | 1.0758204132231406 | 1.035600413223141 | 1.4967084297520663 | 6.990936139669494e-3 | 2.0202048113462827 | 6.0147540204547525 | 1.5426212514576259 | 1.8957523749290006 | 2.3811342709100782 | 1.2420230478769811 | 1.3768632375544787 | 1.5430924375778912 |
| user_002 | Walking | 1.446034828033e12 | -2.6435 | -5.74745 | 0.152682 | -4.0752890909090915 | -8.624956363636365 | 1.2186831818181818 | 6.98809225 | 33.0331815025 | 2.3311793124000002e-2 | 6.328079135537418 | 9.750752081507738 | 1.4317890909090916 | 2.877506363636365 | 1.0660011818181818 | 1.0080471074380166 | 1.4938607438016531 | 1.4210179008264463 | 2.050020000846283 | 8.280042872767776 | 1.1363585196377604 | 1.3242157561663412 | 3.201710657197371 | 2.171397846632907 | 1.1507457391475935 | 1.7893324613378507 | 1.4735663699450077 |
| user_002 | Walking | 1.446034828163e12 | -11.61046 | -7.5485 | -2.87462 | -4.831245454545454 | -8.384582727272727 | 0.42440772727272713 | 134.8027814116 | 56.97985224999999 | 8.2634401444 | 14.14376448495944 | 10.057826703916156 | 6.779214545454546 | 0.8360827272727276 | 3.299027727272727 | 1.4549064462809917 | 1.6889461157024792 | 1.7101610991735539 | 45.95774985330249 | 0.6990343268438022 | 10.883583945314255 | 5.289124424692262 | 3.6797723792280994 | 3.4388567830692605 | 2.2998096496650025 | 1.9182732806428022 | 1.8544154828595614 |
| user_002 | Walking | 1.446034828875e12 | -6.59049 | -10.001 | 2.2603 | -4.716283636363637 | -8.656308181818181 | 1.6854954545454546 | 43.4345584401 | 100.020001 | 5.1089560899999995 | 12.188663402116738 | 10.159951351680348 | 1.8742063636363628 | 1.3446918181818184 | 0.5748045454545454 | 1.0745523966942143 | 0.7261864462809916 | 1.6883109504132234 | 3.5126494934950383 | 1.8081960858851245 | 0.33040026547520657 | 2.126381772822388 | 1.4435771498900067 | 3.2213650308039323 | 1.458211840859341 | 1.2014895546320854 | 1.7948161551545976 |
| user_002 | Walking | 1.446034829458e12 | -7.77842 | -11.53382 | -3.10454 | -5.451335454545454 | -8.53438 | -0.36986681818181827 | 60.50381769639999 | 133.0290037924 | 9.638168611600001 | 14.253806161878307 | 10.580959558448754 | 2.3270845454545457 | 2.99944 | 2.7346731818181818 | 1.7418322314049588 | 2.3492560330578502 | 1.7994710826446279 | 5.41532248169339 | 8.996640313599999 | 7.4784374113555785 | 5.1388778266 | 7.181502582140792 | 4.010739710891808 | 2.2669093115076304 | 2.679832566064677 | 2.002683127929081 |
| user_002 | Walking | 1.446034829523e12 | -6.36057 | -9.27292 | 0.114362 | -5.49313909090909 | -8.673727272727273 | -0.5719193636363636 | 40.4568507249 | 85.98704532639998 | 1.3078667044000002e-2 | 11.245309009464524 | 10.693310550945267 | 0.8674309090909098 | 0.599192727272726 | 0.6862813636363636 | 1.7545000000000002 | 2.340705041322313 | 1.7602007107438014 | 0.7524363820462823 | 0.35903192441652737 | 0.47098211007458674 | 5.159089458684147 | 7.170450883966711 | 3.9398748789330735 | 2.2713629077459525 | 2.6777697593270995 | 1.9849118063362599 |
| user_002 | Walking | 1.446034829847e12 | -4.78944 | -7.93171 | 1.57053 | -5.197027272727273 | -7.489281818181819 | 9.694372727272715e-2 | 22.938735513599998 | 62.9120235241 | 2.4665644809 | 9.397729700230796 | 9.526478401941192 | 0.40758727272727313 | 0.44242818181818055 | 1.4735862727272728 | 1.7579834710743802 | 1.5758842148760324 | 2.13611873553719 | 0.16612738488925652 | 0.19574269606694103 | 2.1714565031702566 | 5.278581932177011 | 4.550054435157998 | 5.180623963482623 | 2.2975164704909106 | 2.133085660529834 | 2.276098408127958 |
| user_002 | Walking | 1.44603483056e12 | -3.29495 | -6.28393 | -1.03525 | -4.510747272727272 | -8.311425454545455 | 0.9156044545454546 | 10.856695502500001 | 39.4877762449 | 1.0717425625 | 7.170510045310585 | 9.596072357280143 | 1.2157972727272721 | 2.0274954545454555 | 1.9508544545454547 | 1.1100220661157023 | 1.3719293388429754 | 1.1581606198347107 | 1.4781630083710728 | 4.110737818202483 | 3.8058331028198435 | 1.5577960981054089 | 2.547191345376184 | 1.6082008242519343 | 1.248117020998195 | 1.5959922761016683 | 1.2681485812995 |
| user_002 | Walking | 1.446034831725e12 | -3.25663 | -5.17264 | 0.191003 | -4.597840000000001 | -8.384581818181816 | 1.117657090909091 | 10.6056389569 | 26.756204569600005 | 3.6482146009e-2 | 6.1154170481259085 | 9.767779631531747 | 1.3412100000000007 | 3.211941818181816 | 0.926654090909091 | 0.85096347107438 | 1.5090608264462808 | 1.1914133884297522 | 1.7988442641000018 | 10.31657024338511 | 0.858687804198554 | 1.1504306786423737 | 3.3610398385510134 | 1.7135853103708756 | 1.0725813156317676 | 1.8333138952593506 | 1.3090398429272028 |
| user_002 | Walking | 1.446034832243e12 | -4.7128 | -7.47186 | 2.37526 | -4.772022636363636 | -7.799326363636365 | 0.2432588181818182 | 22.21048384 | 55.828691859600006 | 5.6418600676 | 9.147733914319984 | 9.536502645012181 | 5.922263636363656e-2 | 0.32746636363636483 | 2.1320011818181817 | 1.9039803057851237 | 1.374779504132231 | 2.022108826446281 | 3.5073206578595272e-3 | 0.10723421931322392 | 4.545429039274123 | 6.152604812177672 | 3.127831639891359 | 4.8022480817657085 | 2.4804444787532884 | 1.7685676803253414 | 2.1914032220852713 |
| user_002 | Walking | 1.446034832502e12 | -6.09233 | -7.66346 | 2.49022 | -5.242317181818181 | -8.408967272727272 | 1.103724818181818 | 37.11648482889999 | 58.728619171599995 | 6.2011956484 | 10.101796852486194 | 10.36651917830248 | 0.8500128181818187 | 0.7455072727272727 | 1.3864951818181819 | 1.7443641074380167 | 0.747087520661157 | 1.8982811404958675 | 0.7225217910733976 | 0.5557810936892561 | 1.9223688892050332 | 5.805616623620921 | 1.0899599468688959 | 4.323321920912323 | 2.409484721599396 | 1.0440114687439481 | 2.0792599454883756 |
| user_002 | Walking | 1.446034832762e12 | -6.36057 | -10.001 | -1.57173 | -4.653577272727273 | -8.948935454545454 | 1.5775034545454545 | 40.4568507249 | 100.020001 | 2.4703351929000004 | 11.956052313276318 | 10.352091282116596 | 1.706992727272727 | 1.0520645454545452 | 3.1492334545454543 | 0.9231695702479339 | 0.9627585950413223 | 1.4726406363636364 | 2.9138241709619823 | 1.1068398078024788 | 9.917671351228297 | 1.1476728268059877 | 1.8532215291447791 | 2.801766501649177 | 1.0712949298890515 | 1.3613307934314787 | 1.673847813168562 |
| user_002 | Walking | 1.446034832827e12 | -5.24928 | -7.85507 | -0.268841 | -4.817309090909091 | -8.875779090909091 | 1.3859015454545451 | 27.5549405184 | 61.70212470490001 | 7.2275483281e-2 | 9.451420036512028 | 10.347610977442356 | 0.43197090909090896 | 1.020709090909091 | 1.6547425454545452 | 0.8424115619834711 | 1.0308480991735536 | 1.4457212892561981 | 0.18659886630082634 | 1.041847048264463 | 2.7381728917373875 | 1.006162191425972 | 1.9412226945685958 | 2.704706160004506 | 1.0030763637061597 | 1.3932776803525548 | 1.6445990879252324 |
| user_002 | Walking | 1.446034833215e12 | -6.62882 | -12.60678 | -0.1922 | -5.82757090909091 | -8.454254545454544 | -0.4465073636363636 | 43.9412545924 | 158.9309019684 | 3.694084e-2 | 14.24461643572055 | 10.675288823021814 | 0.8012490909090904 | 4.152525454545456 | 0.25430736363636364 | 1.6712099256198343 | 2.2244780165289257 | 1.5765175950413224 | 0.6420001056826438 | 17.243467650647947 | 6.467223519967769e-2 | 5.0900214959958845 | 6.285847138249138 | 3.7034431770774034 | 2.2561075984969965 | 2.5071591768870873 | 1.9244332093053798 |
| user_002 | Walking | 1.44603483328e12 | -4.17632 | -7.47186 | 1.76214 | -5.677773636363636 | -8.217365454545455 | -0.47437645454545446 | 17.441648742399998 | 55.828691859600006 | 3.1051373796000004 | 8.739306493172098 | 10.39504900334794 | 1.5014536363636362 | 0.7455054545454542 | 2.2365164545454546 | 1.7221975206611566 | 2.1643059504132234 | 1.7320160578512398 | 2.2543630221495863 | 0.5557783827570244 | 5.00200585145257 | 5.214535432635267 | 6.156302478301128 | 4.133015427240493 | 2.28353573053615 | 2.4811897304118298 | 2.032981905290968 |
| user_002 | Walking | 1.446034834316e12 | -8.19995 | -6.82042 | -3.60271 | -4.869563636363637 | -8.112857272727272 | 0.19796890909090903 | 67.23918000249999 | 46.51812897640001 | 12.9795193441 | 11.257745259287049 | 9.746355230166591 | 3.3303863636363626 | 1.2924372727272715 | 3.800678909090909 | 1.2389174380165289 | 1.747217438016529 | 1.4894252975206612 | 11.091473331095035 | 1.6703941039347074 | 14.445160170008464 | 2.453349205445304 | 4.032061549782646 | 3.2808614411859662 | 1.566317083302517 | 2.007999389886024 | 1.811314837676202 |
| user_002 | Walking | 1.446034834705e12 | -3.98471 | -7.85507 | 1.95374 | -4.810341818181818 | -7.830679090909092 | -0.25142272727272735 | 15.877913784100002 | 61.70212470490001 | 3.8170999876000002 | 9.022036271075395 | 9.530777752011884 | 0.8256318181818174 | 2.4390909090908686e-2 | 2.2051627272727274 | 1.900814049586777 | 1.451421900826446 | 1.9977238677685953 | 0.6816678991942136 | 5.949164462809719e-4 | 4.8627426537528935 | 5.492505319910142 | 3.779321850379338 | 4.7113542709055585 | 2.3436094640340874 | 1.944047800435817 | 2.1705654265434062 |
| user_002 | Walking | 1.446034834835e12 | -4.138 | -8.27659 | 1.26397 | -4.897434545454544 | -7.976993636363636 | 7.255754545454543e-2 | 17.123044 | 68.5019420281 | 1.5976201609 | 9.339304373934924 | 9.709085449764586 | 0.7594345454545444 | 0.29959636363636477 | 1.1914124545454545 | 1.7510161157024788 | 1.2417686776859502 | 1.9524360743801652 | 0.5767408288297504 | 8.975798110413291e-2 | 1.4194636368460247 | 4.953117837237489 | 3.3431550872293765 | 4.573453414821659 | 2.225560117641734 | 1.8284296779557525 | 2.1385633997666886 |
| user_002 | Walking | 1.446034834964e12 | -5.63249 | -7.62514 | 2.22198 | -5.1621927272727275 | -8.429870000000001 | 0.5324010909090909 | 31.724943600099998 | 58.1427600196 | 4.937195120399999 | 9.736780717470225 | 10.26159981256484 | 0.4702972727272723 | 0.8047300000000011 | 1.6895789090909088 | 1.654741322314049 | 0.785725123966942 | 1.952752859504132 | 0.22117952473471034 | 0.6475903729000017 | 2.8546768900448254 | 4.7563571161466545 | 1.769143344757024 | 4.538077592130551 | 2.1809074065963125 | 1.3300914798452863 | 2.13027641214246 |
| user_002 | Walking | 1.446034835223e12 | -4.82776 | -11.18893 | -0.498763 | -4.374884545454545 | -8.88623090909091 | 1.4590569090909091 | 23.307266617599996 | 125.19215454489998 | 0.24876453016900002 | 12.196236538074727 | 10.075582660166956 | 0.4528754545454543 | 2.3026990909090888 | 1.9578199090909092 | 0.8740842148760323 | 0.7223863636363637 | 1.4618730826446282 | 0.20509617732975186 | 5.302423103273544 | 3.833058796432736 | 1.0137865198160023 | 1.0150690150525163 | 2.5147057999277225 | 1.0068696637678594 | 1.007506334993739 | 1.5857823936239557 |
| user_002 | Walking | 1.446034835482e12 | -3.71647 | -5.70913 | -0.460442 | -4.556035454545455 | -8.419419090909091 | 0.7588402727272726 | 13.812149260900002 | 32.5941653569 | 0.21200683536400003 | 6.8277610864150775 | 9.687660360279123 | 0.839565454545455 | 2.7102890909090913 | 1.2192822727272725 | 0.934255950413223 | 1.3196757024793389 | 1.178429289256198 | 0.7048701524661164 | 7.345666956300828 | 1.486649260586983 | 1.0280910441293012 | 2.508477362227648 | 1.5801868474642882 | 1.0139482452912976 | 1.5838173386560863 | 1.2570548307310576 |
| user_002 | Walking | 1.446034835611e12 | -10.72909 | -7.58682 | -2.4531 | -5.315474545454546 | -8.182529090909092 | -4.936972727272729e-2 | 115.11337222809999 | 57.559837712400004 | 6.01769961 | 13.367531916943381 | 10.060971157846659 | 5.413615454545454 | 0.5957090909090921 | 2.4037302727272727 | 1.3684474380165288 | 1.5210953719008267 | 1.5347137603305787 | 29.30723228969338 | 0.3548693209917369 | 5.777919224025529 | 3.662542801412321 | 2.9283213473789633 | 3.2734116543216483 | 1.9137771033775905 | 1.7112338669448321 | 1.8092572106590175 |
| user_002 | Walking | 1.446034835806e12 | -2.79678 | -8.39155 | 1.49389 | -5.158708181818182 | -8.328843636363636 | -0.5649530909090908 | 7.8219783684 | 70.4181114025 | 2.2317073320999996 | 8.970607398777409 | 10.085554878572792 | 2.3619281818181816 | 6.270636363636406e-2 | 2.058843090909091 | 1.6043860330578512 | 1.6721590082644626 | 1.6062863223140498 | 5.578704736066942 | 3.932088040495921e-3 | 4.238834872984099 | 4.355489305518933 | 4.2800591885492105 | 3.456867088232513 | 2.086980906841012 | 2.0688303914408284 | 1.859265201156767 |
| user_002 | Walking | 1.446034836e12 | -4.48288 | -7.70178 | 1.60885 | -4.939237272727273 | -7.7540390909090915 | -0.12601163636363627 | 20.0962130944 | 59.317415168400004 | 2.5883983225 | 9.055497036899741 | 9.489343103602614 | 0.45635727272727333 | 5.22590909090912e-2 | 1.7348616363636362 | 1.6113538842975208 | 1.3535628099173558 | 1.7678014380165292 | 0.20826196037107494 | 2.7310125826446583e-3 | 3.0097448973263137 | 4.322254260129526 | 3.6401935870410216 | 3.9131549599215942 | 2.0790031890618943 | 1.9079291357492871 | 1.9781695983715841 |
| user_002 | Walking | 1.446034836129e12 | -4.25296 | -8.19995 | 1.26397 | -5.148257272727273 | -8.036216363636363 | 0.11784463636363643 | 18.0876687616 | 67.23918000249999 | 1.5976201609 | 9.323329283308619 | 9.852359163043163 | 0.895297272727273 | 0.16373363636363614 | 1.1461253636363635 | 1.48625826446281 | 1.005196611570248 | 1.7918702727272728 | 0.8015572065528931 | 2.680870367685943e-2 | 1.3136033491705865 | 4.081654839446956 | 2.8129625476084903 | 3.9660054813374113 | 2.02031057994729 | 1.6771888825080168 | 1.9914832365193063 |
| user_002 | Walking | 1.446034836582e12 | -4.3296 | -8.27659 | -0.498763 | -4.566487272727272 | -8.792170909090911 | 1.2117159999999998 | 18.74543616 | 68.5019420281 | 0.24876453016900002 | 9.353937284281363 | 10.081521132070447 | 0.23688727272727217 | 0.5155809090909109 | 1.7104789999999999 | 0.7749562809917356 | 0.74487214876033 | 1.4666229173553718 | 5.6115579980165024e-2 | 0.2658236738190102 | 2.9257384094409997 | 0.7472658687811421 | 1.0655498592885786 | 2.457075110637859 | 0.8644454111053758 | 1.0322547453456334 | 1.567506016140882 |
| user_003 | Jogging | 1.446047736537e12 | -3.79311 | -17.58843 | 1.07237 | -4.322629999999999 | -7.806293636363635 | -0.48134418181818184 | 14.387683472099999 | 309.35286986489996 | 1.1499774169 | 18.02471999099847 | 10.694218134861837 | 0.5295199999999993 | 9.782136363636365 | 1.5537141818181819 | 2.413863404958678 | 7.594391074380166 | 1.2895889338842974 | 0.2803914303999993 | 95.69019183677689 | 2.414027758782942 | 7.515907616574665 | 65.83907304479308 | 2.4391224589502865 | 2.7415155692745325 | 8.114127990412346 | 1.561769015875999 |
| user_003 | Jogging | 1.446047736926e12 | -3.37159 | -17.51178 | 4.82776 | -4.517715 | -8.084985454545453 | -0.4778608181818181 | 11.3676191281 | 306.6624387684001 | 23.307266617599996 | 18.475316628250248 | 11.198932071634271 | 1.146125 | 9.426794545454548 | 5.305620818181818 | 2.7058580826446286 | 7.278010578512397 | 1.8004215041322313 | 1.3136025156250002 | 88.86445540221162 | 28.1496122663243 | 10.19237983394395 | 65.20910954449526 | 6.237681883587319 | 3.192550678367369 | 8.0752157583866 | 2.4975351616318275 |
| user_003 | Jogging | 1.446047737509e12 | 0.805325 | -6.66714 | -0.307161 | -5.141291727272728 | -10.283180000000002 | 0.5881397272727272 | 0.6485483556249999 | 44.4507557796 | 9.434787992100001e-2 | 6.722622406111027 | 12.342135019569637 | 5.946616727272728 | 3.6160400000000017 | 0.8953007272727272 | 2.9326126446280996 | 6.638598429752066 | 1.9714372066115702 | 35.36225050107981 | 13.075745281600012 | 0.8015633922550743 | 11.963098083649434 | 54.85210326756357 | 5.456514167173121 | 3.4587711811638298 | 7.406220579186362 | 2.3359182706535604 |
| user_003 | Jogging | 1.446047737572e12 | -2.72014 | 2.14654 | -4.36911 | -4.8591144545454545 | -9.481936363636365 | -5.993636363636024e-4 | 7.399161619599999 | 4.607633971599999 | 19.0891221921 | 5.576371381400274 | 12.021803579777838 | 2.1389744545454548 | 11.628476363636365 | 4.368510636363636 | 3.0089368925619837 | 7.551319421487604 | 2.11363347107438 | 4.575211717198026 | 135.22146253964962 | 19.08388518002222 | 12.225530017785974 | 66.91555652924471 | 6.476468400788093 | 3.496502540795012 | 8.180192939609965 | 2.5448906461355256 |
| user_003 | Jogging | 1.446047739839e12 | -0.267643 | 0.1922 | 0.842448 | -4.5908716363636355 | -9.46800272727273 | -0.2932267272727272 | 7.163277544900001e-2 | 3.694084e-2 | 0.7097186327039999 | 0.9045950741370417 | 11.756586232329614 | 4.323228636363636 | 9.660202727272729 | 1.1356747272727272 | 1.7814189669421487 | 6.562591016528926 | 1.6151536528925623 | 18.690305842274583 | 93.31951673200747 | 1.2897570861659833 | 4.837698738274998 | 62.899088305316646 | 3.8654837329178737 | 2.1994769237877896 | 7.930894546349525 | 1.9660833484157973 |
| user_003 | Jogging | 1.446047740745e12 | -6.32225 | -13.18159 | 1.60885 | -4.6709950909090905 | -7.7017857272727275 | -0.4848285454545453 | 39.970845062500004 | 173.7543149281 | 2.5883983225 | 14.707602058564815 | 10.600213077768727 | 1.6512549090909099 | 5.479804272727272 | 2.0936785454545452 | 2.8407704297520664 | 7.392020719008266 | 1.7228301570247935 | 2.726642774796829 | 30.028254867400072 | 4.38348985169666 | 11.506518091074243 | 62.0801857586161 | 3.4522606063038457 | 3.392125895522488 | 7.879098029509222 | 1.8580259972088242 |
| user_003 | Jogging | 1.446047740875e12 | -4.36792 | -13.06663 | -0.11556 | -4.329596 | -8.579669363636363 | -6.678918181818164e-2 | 19.0787251264 | 170.7368195569 | 1.3354113599999998e-2 | 13.777840861212617 | 11.223379404090814 | 3.832400000000025e-2 | 4.486960636363637 | 4.877081818181836e-2 | 2.4715021983471073 | 7.860414801652894 | 1.935966694214876 | 1.468728976000019e-3 | 20.132815752276777 | 2.378592706123984e-3 | 10.326157866923786 | 69.21213340950345 | 4.851537168392439 | 3.2134339680354076 | 8.319382994519692 | 2.2026205230117237 |
| user_003 | Jogging | 1.44604774094e12 | -7.89339 | -0.957409 | -2.10822 | -4.552550545454546 | -8.809591090909091 | -3.892009090909087e-2 | 62.3056056921 | 0.9166319932809999 | 4.444591568400001 | 8.225985002039634 | 11.411497501602561 | 3.3408394545454545 | 7.852182090909091 | 2.069299909090909 | 2.672604776859504 | 7.591539454545455 | 1.9856882727272724 | 11.16120826104757 | 61.65676358879346 | 4.282002113763645 | 11.224996006406185 | 64.19438356391187 | 5.030120295116148 | 3.3503725175577395 | 8.012139761880833 | 2.242792967510855 |
| user_003 | Jogging | 1.446047741911e12 | -9.92436 | -19.08292 | -5.13552 | -4.942721545454545 | -8.123307727272728 | -0.7461035454545454 | 98.4929214096 | 364.15783572640004 | 26.373565670399998 | 22.113894338320424 | 11.071813299428175 | 4.981638454545455 | 10.959612272727274 | 4.389416454545454 | 2.4198804876033058 | 7.12631 | 1.7234634462809917 | 24.816721691806027 | 120.11310116851428 | 19.266976811434386 | 8.522939200738632 | 63.12595207200314 | 4.573635273326528 | 2.9194073372413505 | 7.945184206297745 | 2.1386059181921593 |
| user_003 | Jogging | 1.446047741975e12 | -6.62882 | -9.73276 | -1.64837 | -5.106454272727272 | -8.276588636363636 | -0.8575806363636364 | 43.9412545924 | 94.72661721760001 | 2.7171236568999997 | 11.890542269673826 | 11.29883403289232 | 1.522365727272728 | 1.456171363636365 | 0.7907893636363635 | 2.5566939834710745 | 7.205801041322314 | 1.7798353801652893 | 2.317597407574622 | 2.1204350402745904 | 0.6253478176404048 | 8.733602295846394 | 63.28795014592597 | 4.6278361805447465 | 2.9552668738789722 | 7.9553724077459735 | 2.1512406142839406 |
| user_003 | Jogging | 1.44604774217e12 | -2.14534 | -5.47921 | 0.191003 | -4.761570909090909 | -7.970025999999999 | -0.8262272727272726 | 4.6024837156 | 30.021742224100002 | 3.6482146009e-2 | 5.887334548478539 | 10.68804051784389 | 2.616230909090909 | 2.490815999999999 | 1.0172302727272726 | 2.4186141487603305 | 6.995830983471072 | 1.7988368677685949 | 6.844664169682645 | 6.204164345855994 | 1.0347574277528013 | 7.453212085243059 | 59.74840323166583 | 4.752632291847676 | 2.730057157871069 | 7.729709129822792 | 2.1800532772956895 |
| user_003 | Jogging | 1.44604774327e12 | -2.29862 | -1.18733 | 1.30229 | -4.639641818181818 | -8.698112363636362 | -2.150190909090909e-2 | 5.2836539044 | 1.4097525289 | 1.6959592440999998 | 2.8964401732816785 | 11.391576321113966 | 2.3410218181818183 | 7.510782363636362 | 1.323791909090909 | 2.8230356198347106 | 7.835078702479337 | 1.5758826859504131 | 5.480383153203307 | 56.41185171391101 | 1.7524250185745534 | 12.127020067467846 | 69.08815603044107 | 2.9333404785628705 | 3.4823871219994835 | 8.311928538578822 | 1.7126997631116991 |
| user_003 | Jogging | 1.446047744371e12 | -5.78577 | -1.45557 | -0.498763 | -4.413205 | -8.283557454545454 | -0.5022474545454546 | 33.475134492900004 | 2.1186840249000003 | 0.24876453016900002 | 5.986867548891407 | 11.395727898766877 | 1.3725650000000007 | 6.827987454545454 | 3.484454545454596e-3 | 2.815434545454546 | 7.698899520661155 | 1.4970256611570247 | 1.883934679225002 | 46.62141267943011 | 1.2141423479339196e-5 | 11.479412855986366 | 69.20795231467186 | 3.978002070082623 | 3.388128223073378 | 8.319131704371067 | 1.994492935581027 |
| user_003 | Jogging | 1.446047744436e12 | -5.01936 | -15.48081 | -0.690364 | -4.824277272727273 | -8.046668363636364 | -1.014346 | 25.193974809599997 | 239.6554782561 | 0.476602452496 | 16.288832233103637 | 11.17140295338651 | 0.19508272727272669 | 7.434141636363636 | 0.323982 | 2.509188512396695 | 7.497797024793388 | 1.0501667107438017 | 3.805727048016506e-2 | 55.266461869515396 | 0.104964336324 | 10.328273272895231 | 65.77303908697071 | 1.4919414199285874 | 3.2137631015517045 | 8.110057896647268 | 1.2214505392886719 |
| user_003 | Jogging | 1.44604774573e12 | 0.996927 | -4.48288 | -0.383802 | -4.552551363636363 | -9.85468909090909 | -1.8017909090909088e-2 | 0.993863443329 | 20.0962130944 | 0.147303975204 | 4.6084032498179885 | 11.986882630018972 | 5.549478363636363 | 5.371809090909091 | 0.36578409090909086 | 2.4214637024793393 | 6.722205024793388 | 1.3785808512396693 | 30.79671010846813 | 28.856332909173553 | 0.13379800116219004 | 8.259204611238276 | 51.4197099762798 | 3.0440588920920715 | 2.873883193736008 | 7.170753794147433 | 1.7447231562892926 |
| user_003 | Jogging | 1.44604774806e12 | -2.56686 | 0.15388 | 1.07237 | -4.144961818181818 | -8.792171272727273 | -0.7495882727272728 | 6.5887702596 | 2.3679054399999996e-2 | 1.1499774169 | 2.7861131942008384 | 11.509337901370538 | 1.5781018181818176 | 8.946051272727273 | 1.8219582727272727 | 2.2149777685950416 | 7.928504413223141 | 1.5635328512396696 | 2.4904053485487587 | 80.03183337426525 | 3.319531947559347 | 7.712193560506762 | 75.07473299134091 | 3.8857866139461805 | 2.777083643051963 | 8.664567674808762 | 1.9712398671765394 |
| user_003 | Jogging | 1.446047748385e12 | -6.66714 | 1.95493 | -1.53341 | -4.695380909090909 | -9.356524545454546 | -1.0143462727272725 | 44.4507557796 | 3.8217513049000003 | 2.3513462280999997 | 7.115044153945919 | 11.895361102994752 | 1.9717590909090905 | 11.311454545454545 | 0.5190637272727274 | 1.989806033057851 | 7.644426099173552 | 1.1416929338842976 | 3.887833912582643 | 127.94900393388428 | 0.2694271529702564 | 6.719062631443501 | 68.53222791279427 | 2.551984568505031 | 2.5921154741723025 | 8.278419409089773 | 1.5974932139151738 |
| user_003 | Jogging | 1.446047749162e12 | -6.93538 | 1.53341 | -2.18486 | -4.002132181818182 | -8.816556454545454 | -0.8506143636363636 | 48.0994957444 | 2.3513462280999997 | 4.7736132196000005 | 7.431315845265898 | 11.428345861594188 | 2.933247818181818 | 10.349966454545454 | 1.3342456363636366 | 1.8856133553719012 | 7.412287446280991 | 1.6506236694214877 | 8.603942762868396 | 107.1218056102162 | 1.7802114181554056 | 5.191939889264487 | 69.00963976674144 | 4.4900357652589316 | 2.278582868641052 | 8.307204088424784 | 2.118970449359531 |
| user_003 | Jogging | 1.446047749485e12 | 0.881966 | 2.10822 | -2.8363 | -3.9150412727272723 | -9.189308272727272 | -0.27929181818181825 | 0.7778640251560001 | 4.444591568400001 | 8.04459769 | 3.64239664006489 | 11.629436141235779 | 4.797007272727273 | 11.297528272727273 | 2.5570081818181816 | 2.039845504132231 | 7.113958429752066 | 1.6395383305785123 | 23.011278774598345 | 127.63414507307209 | 6.538290841885122 | 6.35398911787781 | 63.80273434936145 | 4.819507974835495 | 2.5207120259715925 | 7.987661381741307 | 2.1953377814895583 |
| user_003 | Jogging | 1.446047749744e12 | -6.9737 | -13.21991 | -1.72501 | -4.176315818181819 | -8.924550090909092 | -0.23400354545454552 | 48.63249169 | 174.76602040810002 | 2.9756595001 | 15.04573599390206 | 11.555733771422016 | 2.797384181818181 | 4.295359909090909 | 1.4910064545454544 | 1.91886694214876 | 7.525665933884297 | 1.7240961404958677 | 7.825358260686574 | 18.450116748625458 | 2.2231002474962063 | 5.411166328523052 | 64.81778558076684 | 3.8399872278514042 | 2.326191378309844 | 8.050949358974185 | 1.959588535343939 |
| user_003 | Jogging | 1.446047749939e12 | -1.95374 | -4.55952 | 0.957409 | -4.2529567272727276 | -8.377614545454545 | -0.40121909090909097 | 3.8170999876000002 | 20.7892226304 | 0.9166319932809999 | 5.052024803114193 | 10.957703308010748 | 2.2992167272727277 | 3.818094545454545 | 1.3586280909090909 | 2.076581074380165 | 7.216253289256199 | 1.5008247272727273 | 5.286397558970713 | 14.577845958029748 | 1.845870289407281 | 5.86303889239928 | 61.4850680841794 | 2.547085777173806 | 2.421371283467135 | 7.841241488704413 | 1.5959592028538216 |
| user_003 | Jogging | 1.446047750521e12 | -6.59049 | -6.36057 | -2.2615 | -4.287792636363637 | -9.022092818181818 | -1.0352465454545454 | 43.4345584401 | 40.4568507249 | 5.114382249999999 | 9.4342880714445 | 11.590975913207851 | 2.302697363636363 | 2.661522818181818 | 1.2262534545454544 | 2.49778626446281 | 7.295744867768596 | 1.603118561983471 | 5.302415148497857 | 7.083703711702487 | 1.5036975347846608 | 8.97714823811022 | 64.82860298871549 | 3.651281482976789 | 2.996188952337656 | 8.051621140411134 | 1.910832667445475 |
| user_003 | Jogging | 1.446047750651e12 | -4.138 | 1.34181 | -1.26517 | -4.343531727272727 | -8.823523727272727 | -1.1815609999999999 | 17.123044 | 1.8004540760999999 | 1.6006551288999997 | 4.530359059169593 | 11.379730018195447 | 0.20553172727272706 | 10.165333727272728 | 8.360900000000004e-2 | 2.1814061818181822 | 7.551319429752066 | 1.3573624214876032 | 4.2243290915710656e-2 | 103.33400978682845 | 6.990464881000007e-3 | 7.960315025729009 | 66.70365085604918 | 3.264274458154972 | 2.821403024335412 | 8.167230305069717 | 1.806730322476205 |
| user_003 | Jogging | 1.44604775091e12 | -2.10702 | -9.08132 | -7.7239e-2 | -4.172831909090909 | -9.767598181818183 | -0.5684371818181818 | 4.439533280399999 | 82.47037294239999 | 5.965863121e-3 | 9.322868232787643 | 12.052469662406963 | 2.065811909090909 | 0.6862781818181833 | 0.4911981818181818 | 1.941033305785124 | 6.732975190082642 | 1.4982938347107437 | 4.267578843741825 | 0.47097774283967153 | 0.2412756538214876 | 5.0293433435451735 | 58.48312244410238 | 4.130988480262454 | 2.2426197501014684 | 7.6474258704548665 | 2.032483328409474 |
| user_003 | Jumping | 1.446047778488e12 | 0.805325 | -10.92069 | -1.49509 | -3.045865625 | -10.45605625 | -2.8985725 | 0.6485483556249999 | 119.26147007610001 | 2.2352941081 | 11.051937049215626 | 11.40789343159099 | 3.8511906249999996 | 0.46463375000000084 | 1.4034825000000002 | 1.2694057180059524 | 2.5898877395833337 | 0.9794885 | 14.831669230087888 | 0.2158845216390633 | 1.9697631278062506 | 2.994031096828895 | 19.251323506625305 | 2.225648644605465 | 1.7303268757170984 | 4.387633018681633 | 1.491860799339357 |
| user_003 | Jumping | 1.446047778682e12 | 0.1922 | -0.650847 | -7.7239e-2 | -2.7480122727272724 | -7.510182272727272 | -2.1639553636363638 | 3.694084e-2 | 0.42360181740899994 | 5.965863121e-3 | 0.6830142901360117 | 9.06147948144217 | 2.9402122727272726 | 6.8593352727272725 | 2.0867163636363637 | 1.4732926126361015 | 4.2381636859044995 | 1.2864592148760332 | 8.644848208696073 | 47.05048038368052 | 4.354385182267769 | 3.4053383334628764 | 34.89465889164739 | 3.5019785473636813 | 1.8453558826044576 | 5.907170125504038 | 1.8713574077026764 |
| user_003 | Jumping | 1.446047779653e12 | 0.422122 | -0.114362 | 0.344284 | -2.6922735454545452 | -7.0398891818181815 | -1.9375156363636363 | 0.178186982884 | 1.3078667044000002e-2 | 0.11853147265599999 | 0.5565942171672286 | 8.651382373881274 | 3.114395545454545 | 6.925527181818182 | 2.2817996363636364 | 1.8491923636363636 | 5.632452041322314 | 1.3953661900826448 | 9.699459613547113 | 47.96292674610248 | 5.206609580509223 | 4.305371535261241 | 45.7487659233511 | 2.6415311291557284 | 2.074938923260451 | 6.76378340304826 | 1.6252787850568062 |
| user_003 | Jumping | 1.446047779912e12 | -2.68182 | -13.21991 | -3.18118 | -2.417064454545454 | -9.300787 | -2.2057583636363636 | 7.192158512400001 | 174.76602040810002 | 10.1199061924 | 13.859223827938562 | 10.676319564800322 | 0.26475554545454605 | 3.919123000000001 | 0.9754216363636363 | 1.6018517272727266 | 7.413555760330579 | 1.5780995454545452 | 7.009549884893419e-2 | 15.359525089129006 | 0.951447368686314 | 3.8130799249221172 | 63.45771008430081 | 2.9915428584719663 | 1.9527109168850665 | 7.966034777999705 | 1.7296077180886902 |
| user_003 | Jumping | 1.446047780042e12 | -3.17999 | -3.98471 | -0.728685 | -1.790004727272727 | -7.712235181818182 | -2.0455104545454543 | 10.1123364001 | 15.877913784100002 | 0.530981829225 | 5.1498768930358905 | 9.01807031839871 | 1.389985272727273 | 3.7275251818181814 | 1.3168254545454543 | 1.3507111900826445 | 6.500836636363636 | 1.3830142727272727 | 1.9320590583987116 | 13.894443981088665 | 1.7340292777388424 | 2.6131867887287834 | 54.22806571943482 | 2.498340144855037 | 1.6165354276132593 | 7.3639707848031835 | 1.5806138506463359 |
| user_003 | Jumping | 1.44604778043e12 | -1.99206 | -14.44616 | -3.64103 | -3.058058454545454 | -11.969273636363635 | -3.0209360909090908 | 3.9683030435999997 | 208.6915387456 | 13.257099460900001 | 15.030533631581417 | 12.933403086552534 | 1.0659984545454542 | 2.476886363636366 | 0.6200939090909094 | 1.3953645371900827 | 4.610156082644628 | 1.3288589421487604 | 1.1363527050932967 | 6.134966058367779 | 0.3845164560916451 | 2.8087740605938762 | 33.79768905571308 | 2.244334618817004 | 1.6759397544643053 | 5.81357799085151 | 1.498110349345803 |
| user_003 | Jumping | 1.446047781855e12 | -0.420925 | -2.18366 | -2.68302 | -2.2707519090909094 | -7.266325909090909 | -2.4008437272727274 | 0.177177855625 | 4.768370995600001 | 7.1985963204 | 3.484845071394853 | 8.674993386245026 | 1.8498269090909094 | 5.08266590909091 | 0.2821762727272725 | 1.6468234049586774 | 5.169758603305786 | 1.2481016942148762 | 3.4218595935968277 | 25.833492743434924 | 7.962344889025609e-2 | 3.8184564220586044 | 38.79461440106537 | 2.358978580515499 | 1.9540871070805939 | 6.228532283055565 | 1.5358966698692653 |
| user_003 | Jumping | 1.446047782438e12 | -2.49022 | -8.08499 | -1.80165 | -1.9084485454545452 | -8.353229636363638 | -2.449615818181818 | 6.2011956484 | 65.36706330009999 | 3.2459427224999997 | 8.649520314502995 | 9.039489583394376 | 0.5817714545454546 | 0.26823963636363857 | 0.647965818181818 | 0.9282387107438016 | 3.432042975206612 | 0.7626072479338841 | 0.338458025323934 | 7.195250251649705e-2 | 0.4198597015320328 | 1.4550386777920103 | 22.957360296408897 | 1.026977631594913 | 1.2062498405355377 | 4.791383964619085 | 1.013399048546481 |
| user_003 | Jumping | 1.446047782568e12 | -5.13432 | -17.1669 | -4.67568 | -2.7514953636363635 | -10.690768181818182 | -2.7840481818181813 | 26.361241862399996 | 294.70245560999996 | 21.861983462399998 | 18.518252642590227 | 11.4462039840676 | 2.3828246363636363 | 6.476131818181816 | 1.8916318181818186 | 0.9276052809917356 | 3.080826 | 0.7825592644628098 | 5.677853247661496 | 41.94028332646692 | 3.578270935557853 | 1.4291005865544335 | 20.058100503065358 | 1.1121690544572347 | 1.1954499515054713 | 4.478627077918562 | 1.054594260584247 |
| user_003 | Jumping | 1.446047782762e12 | -2.22198 | -1.68549 | 0.267643 | -2.765430909090909 | -9.600380909090909 | -2.6098642727272723 | 4.937195120399999 | 2.8408765400999996 | 7.163277544900001e-2 | 2.801732399061159 | 10.441885982259416 | 0.5434509090909092 | 7.914890909090909 | 2.8775072727272724 | 0.8313299173553719 | 3.10774579338843 | 1.0194480330578513 | 0.2953388905917357 | 62.64549810280992 | 8.280048104598345 | 1.2023843439890611 | 18.333901454164252 | 2.0604466668327537 | 1.0965328741032168 | 4.281810534594478 | 1.435425604771196 |
| user_003 | Jumping | 1.446047784057e12 | -2.8351 | -17.62674 | -5.02056 | -2.277716909090909 | -7.029436727272728 | -2.5994132727272725 | 8.03779201 | 310.7019630276001 | 25.206022713599996 | 18.54577519952186 | 8.71471938018078 | 0.5573830909090911 | 10.597303272727274 | 2.421146727272727 | 1.3671798595041322 | 6.670267752066116 | 1.7636845867768598 | 0.3106759100313721 | 112.30283665415618 | 5.861951474983438 | 2.8017921626400346 | 55.3477277112353 | 5.347183548621389 | 1.6738554784210118 | 7.4396053464706915 | 2.312397792037821 |
| user_003 | Jumping | 1.446047784122e12 | -3.90807 | -19.19788 | -5.59536 | -2.2986187272727268 | -8.370647636363636 | -2.8850732727272725 | 15.2730111249 | 368.55859649440004 | 31.308053529600002 | 20.37497634719854 | 9.99707912883897 | 1.6094512727272732 | 10.827232363636366 | 2.710286727272728 | 1.3491281157024795 | 7.46264358677686 | 1.9866386363636366 | 2.5903333992834394 | 117.22896065617472 | 7.345654144030713 | 2.7401008385699153 | 65.59974817695337 | 6.0089287608963105 | 1.655324994848418 | 8.099367146694448 | 2.4513116409172273 |
| user_003 | Jumping | 1.446047784252e12 | -1.49389 | -6.7821 | -2.0699 | -2.1836587272727273 | -8.224333090909091 | -2.9861 | 2.2317073320999996 | 45.996880409999996 | 4.28448601 | 7.2465904915415225 | 9.761379035404877 | 0.6897687272727273 | 1.4422330909090917 | 0.9161999999999999 | 1.3491279669421488 | 6.997098214876034 | 1.8818117272727273 | 0.4757808971234381 | 2.080036288513192 | 0.8394224399999998 | 2.7227248763545298 | 60.791994286431425 | 5.7640904127899635 | 1.6500681429427482 | 7.796922103396406 | 2.4008520180948185 |
| user_003 | Jumping | 1.44604778451e12 | -0.344284 | -6.51385 | -2.8363 | -2.0025072727272724 | -7.524116727272728 | -2.6516678181818185 | 0.11853147265599999 | 42.430241822499994 | 8.04459769 | 7.112901727505871 | 8.473331609269454 | 1.6582232727272723 | 1.010266727272728 | 0.18463218181818153 | 1.1065376033057854 | 4.376115950413224 | 1.118888867768595 | 2.7497044222143456 | 1.0206388602343484 | 3.408904256294204e-2 | 1.4812928383542754 | 31.90584041715783 | 2.048208537827977 | 1.2170837433612673 | 5.6485255082329076 | 1.4311563638638432 |
| user_003 | Jumping | 1.446047784769e12 | -2.4519 | 7.7239e-2 | 1.11069 | -2.5703451818181815 | -10.182152818181818 | -3.1358972727272727 | 6.011813610000001 | 5.965863121e-3 | 1.2336322760999998 | 2.6928445460555275 | 11.327773506010578 | 0.11844518181818131 | 10.259391818181818 | 4.246587272727273 | 1.3076407190082644 | 4.473975008264463 | 1.5087420743801652 | 1.4029261095942028e-2 | 105.25512047897604 | 18.033503464889254 | 2.3490630347849053 | 33.81312160041558 | 5.189621264439421 | 1.5326653368511032 | 5.814905123939305 | 2.2780740252325913 |
| user_003 | Jumping | 1.446047785029e12 | -2.29862 | -4.3296 | -4.63736 | -2.2672662727272725 | -6.458115181818181 | -2.658635181818182 | 5.2836539044 | 18.74543616 | 21.505107769600002 | 6.747903217592855 | 8.19639577552533 | 3.135372727272756e-2 | 2.128515181818181 | 1.978724818181818 | 1.3332932148760328 | 5.2077616859504126 | 1.635421950413223 | 9.8305621389258e-4 | 4.530576879230485 | 3.915351906088669 | 2.570783017457629 | 39.53504755990894 | 5.427567003101221 | 1.6033661520244304 | 6.287690160934216 | 2.329713931602166 |
| user_003 | Jumping | 1.446047785352e12 | -2.49022 | -6.51385 | -2.95126 | -1.8840638181818186 | -7.443992454545455 | -2.324203363636364 | 6.2011956484 | 42.430241822499994 | 8.7099355876 | 7.572408669538379 | 8.94181821018264 | 0.6061561818181813 | 0.9301424545454555 | 0.6270566363636361 | 1.1328244958677687 | 6.097996702479341 | 1.6145197190082643 | 0.36742531675639606 | 0.8651649857478448 | 0.39320002520767733 | 1.9553144765137291 | 51.86153301692632 | 3.6813627580764487 | 1.3983255974606663 | 7.201495193147484 | 1.9186877698251086 |
| user_003 | Jumping | 1.446047785611e12 | -2.87342 | -6.62882 | -2.56806 | -2.329972272727272 | -8.562249818181819 | -2.6934724545454545 | 8.2565424964 | 43.9412545924 | 6.5949321636 | 7.667641700836053 | 9.605136865433074 | 0.5434477272727278 | 1.9334298181818186 | 0.12541245454545447 | 0.882950173553719 | 3.814612991735537 | 0.9903100000000002 | 0.29533543227789316 | 3.7381508618345802 | 1.572828375511568e-2 | 1.1643291610345898 | 29.13604241271818 | 1.4706730612943126 | 1.0790408523473938 | 5.397781249061338 | 1.2127130993331905 |
| user_003 | Jumping | 1.446047786324e12 | -0.574206 | -13.29655 | -3.94759 | -2.2428808181818183 | -9.328656272727272 | -3.2055689090909087 | 0.329712530436 | 176.7982419025 | 15.583466808099999 | 13.882053927320554 | 11.040555029331307 | 1.6686748181818183 | 3.9678937272727275 | 0.7420210909090912 | 1.6753259338842974 | 7.225121256198347 | 2.124083900826447 | 2.7844756488341242 | 15.744180630930257 | 0.5505952993539177 | 3.39283596636857 | 57.272907306964505 | 6.280795804892235 | 1.841965245700518 | 7.567886581269867 | 2.5061515925602413 |
| user_003 | Jumping | 1.446047786518e12 | -2.18366 | -8.04667 | -3.06622 | -1.6576244545454544 | -6.674103545454545 | -2.4635480000000003 | 4.768370995600001 | 64.7488980889 | 9.4017050884 | 8.883635189093482 | 8.263893722958313 | 0.5260355454545458 | 1.3725664545454554 | 0.6026719999999997 | 1.5052596115702483 | 5.776549446280992 | 1.707310859504132 | 0.2767133950816615 | 1.8839386721434819 | 0.3632135395839996 | 2.8409857953691744 | 45.87364227916548 | 4.382206722332543 | 1.6855224102245494 | 6.7730083625495014 | 2.0933720936165514 |
| user_003 | Jumping | 1.446047786906e12 | -4.44456 | -19.4278 | -8.62267 | -2.946580545454546 | -11.077455454545456 | -3.81521 | 19.7541135936 | 377.43941284000005 | 74.35043792889999 | 21.715063075259533 | 12.256086381514585 | 1.4979794545454541 | 8.350344545454545 | 4.807459999999999 | 1.266154644628099 | 3.991645636363637 | 1.7177616033057852 | 2.2439424462402964 | 69.72825402780248 | 23.11167165159999 | 2.4407635313636784 | 27.42025693095353 | 5.242709314360911 | 1.562294316498552 | 5.236435517692692 | 2.289696336713869 |
| user_003 | Jumping | 1.446047787035e12 | -2.91174 | 3.41111 | -1.53341 | -2.8211684545454547 | -8.597085454545455 | -3.1115099999999996 | 8.4782298276 | 11.635671432099999 | 2.3513462280999997 | 4.739751838208409 | 10.334117729844163 | 9.057154545454527e-2 | 12.008195454545454 | 1.5780999999999996 | 1.1961645867768596 | 3.850399669421488 | 1.6066013223140494 | 8.20320484602476e-3 | 144.1967580745661 | 2.490399609999999 | 2.4730005861850963 | 28.657848934640707 | 4.375157915209365 | 1.572577688441845 | 5.353302619378126 | 2.091687814949775 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 | -2.1139846363636363 | -7.62514390909091 | -3.205570636363637 | 15.2730111249 | 314.7682737241 | 57.578047520400006 | 19.688050496923253 | 9.358800088769957 | 1.7940853636363636 | 10.116566090909092 | 4.382449363636363 | 1.1537262148760332 | 6.411527363636364 | 2.0167259669421482 | 3.2187422920142232 | 102.34490947173167 | 19.205862424836763 | 2.1654029518886837 | 50.06674660371802 | 6.36506042608091 | 1.4715308192113015 | 7.0757859354080255 | 2.5229071378235286 |
| user_003 | Jumping | 1.446047790337e12 | 1.15021 | -0.919089 | 1.64717 | -2.354358727272727 | -7.262841727272728 | -2.5785121818181818 | 1.3229830441 | 0.8447245899210001 | 2.7131690089 | 2.2092706133294313 | 8.77086651951292 | 3.504568727272727 | 6.343752727272728 | 4.225682181818182 | 1.2636208429752065 | 4.4613078512396696 | 1.713963090909091 | 12.282001964177981 | 40.243198664780174 | 17.856389901735668 | 2.7066131737958865 | 32.5271555808163 | 4.812581558122925 | 1.6451787665162367 | 5.703258330184273 | 2.1937596855906816 |
| user_003 | Jumping | 1.446047791438e12 | -1.30229 | 1.34181 | -1.11189 | -2.3439073636363634 | -6.2386457272727265 | -2.9199100000000002 | 1.6959592440999998 | 1.8004540760999999 | 1.2362993721000002 | 2.1754798763261407 | 7.901043770785439 | 1.0416173636363635 | 7.580455727272726 | 1.8080200000000002 | 1.4647219256198347 | 5.322090123966942 | 1.7671677933884298 | 1.0849667322287682 | 57.463309033141876 | 3.268936320400001 | 3.8035068719434904 | 39.625116056193036 | 5.134791706958699 | 1.9502581552049694 | 6.294848374360818 | 2.2660078788386193 |
| user_003 | Jumping | 1.446047791697e12 | -4.59784 | -13.37319 | -5.02056 | -2.4658354545454544 | -8.840942818181817 | -3.6828327272727277 | 21.140132665599996 | 178.8422107761 | 25.206022713599996 | 15.006277558252078 | 10.518982065674521 | 2.1320045454545453 | 4.532247181818182 | 1.337727272727272 | 1.5153941652892562 | 7.493047033057851 | 2.419879297520661 | 4.545443381838842 | 20.54126451709885 | 1.789514256198345 | 3.95029581043249 | 61.87828044261103 | 7.525001145120626 | 1.987535109232662 | 7.866274877132824 | 2.743173553590918 |
| user_003 | Jumping | 1.446047792085e12 | -4.02303 | -4.06135 | -7.7413 | -2.4031294545454545 | -8.133757090909093 | -3.9092709090909095 | 16.1847703809 | 16.4945638225 | 59.927725689999995 | 9.623256200133092 | 9.876822030790589 | 1.6199005454545459 | 4.072407090909093 | 3.8320290909090904 | 0.9177882479338845 | 4.391951958677687 | 1.5714489669421488 | 2.624077777163935 | 16.58449951408666 | 14.68444695357355 | 1.2166755875009239 | 31.272381047682792 | 4.2373374635131755 | 1.1030301843108936 | 5.592171407215876 | 2.0584794056568008 |
| user_003 | Jumping | 1.446047794093e12 | -2.10702 | -5.93905 | -2.14654 | -2.065214 | -6.7124228181818175 | -2.940811181818182 | 4.439533280399999 | 35.2723149025 | 4.607633971599999 | 6.657287897822957 | 8.3093839032925 | 4.180599999999979e-2 | 0.7733728181818176 | 0.794271181818182 | 0.9722588347107437 | 5.3952462231404965 | 1.9115816198347106 | 1.7477416359999822e-3 | 0.5981055159024866 | 0.6308667102668516 | 1.4342084263393786 | 40.828681700373515 | 5.156018342640205 | 1.1975844130329096 | 6.389732521817601 | 2.2706867557283643 |
| user_003 | Jumping | 1.446047794805e12 | -1.99206 | -18.35483 | -5.78697 | -2.0582473636363634 | -7.384770727272726 | -3.097577727272727 | 3.9683030435999997 | 336.8997843289 | 33.4890217809 | 19.34831024026129 | 9.33222617185624 | 6.618736363636346e-2 | 10.970059272727273 | 2.689392272727273 | 1.305741933884297 | 6.121432487603307 | 2.2140271570247934 | 4.380767105132208e-3 | 120.34220044714962 | 7.232830796605167 | 2.3505269335475982 | 54.14035826919018 | 5.733336804386699 | 1.5331428288152407 | 7.358013201210649 | 2.3944387242914984 |
| user_003 | Sitting | 1.446047527495e12 | 0.422122 | -0.689167 | 9.4262 | 0.453475 | -0.6612976363636364 | 9.440134545454544 | 0.178186982884 | 0.47495115388899994 | 88.85324643999999 | 9.460781393562215 | 9.474509020745439 | 3.135300000000002e-2 | 2.786936363636361e-2 | 1.3934545454544534e-2 | 3.3399114128295944e-2 | 4.658829602518689e-2 | 2.430351620097068e-2 | 9.830106090000013e-4 | 7.767014294958663e-4 | 1.941715570247677e-4 | 2.3041740084988873e-3 | 3.977240666965758e-3 | 8.163654980999173e-4 | 4.8001812554307646e-2 | 6.306536820605868e-2 | 2.857211049432501e-2 |
| user_003 | Sitting | 1.446047528855e12 | 0.460442 | -0.612526 | 9.46452 | 0.45695854545454556 | -0.6612977272727272 | 9.447101818181817 | 0.21200683536400003 | 0.375188100676 | 89.5771388304 | 9.495490180419335 | 9.481359853221797 | 3.4834545454544563e-3 | 4.877172727272716e-2 | 1.7418181818182887e-2 | 2.5969123966942145e-2 | 3.040279338842974e-2 | 1.8368264462810237e-2 | 1.2134455570247312e-5 | 2.3786813811652783e-3 | 3.0339305785127694e-4 | 1.1694653645424491e-3 | 1.2224157735432e-3 | 6.9504591435009e-4 | 3.419744675472789e-2 | 3.496306298857696e-2 | 2.6363723453831214e-2 |
| user_003 | Sitting | 1.446047528985e12 | 0.422122 | -0.689167 | 9.4262 | 0.4604421818181818 | -0.657814 | 9.436650909090908 | 0.178186982884 | 0.47495115388899994 | 88.85324643999999 | 9.460781393562215 | 9.470900946809623 | 3.832018181818181e-2 | 3.1352999999999964e-2 | 1.0450909090907956e-2 | 2.343555371900825e-2 | 3.578669421487602e-2 | 2.3752066115702807e-2 | 1.4684363345785116e-3 | 9.830106089999977e-4 | 1.0922150082642256e-4 | 1.1032685002223886e-3 | 1.7707423253027798e-3 | 9.521025779113177e-4 | 3.321548584956102e-2 | 4.2080189226080957e-2 | 3.0856159480909442e-2 |
| user_003 | Sitting | 1.446047529664e12 | 0.537083 | -0.804128 | 9.4262 | 0.4883118181818181 | -0.6996179090909092 | 9.443618181818183 | 0.28845814888899995 | 0.646621840384 | 88.85324643999999 | 9.475670236414572 | 9.48224125108708 | 4.877118181818185e-2 | 0.1045100909090908 | 1.7418181818182887e-2 | 3.0719801652892575e-2 | 4.1487272727272664e-2 | 2.660231404958728e-2 | 2.378628175942152e-3 | 1.0922359101826424e-2 | 3.0339305785127694e-4 | 1.0690789414845984e-3 | 2.3245799321532615e-3 | 1.1959202716754786e-3 | 3.269677264631172e-2 | 4.821389770754136e-2 | 3.458208021035575e-2 |
| user_003 | Sitting | 1.446047530053e12 | 0.422122 | -0.765807 | 9.38788 | 0.4848279999999999 | -0.7065851818181818 | 9.464519999999998 | 0.178186982884 | 0.586460361249 | 88.13229089439999 | 9.428517287385805 | 9.504046308773319 | 6.270599999999993e-2 | 5.9221818181818264e-2 | 7.663999999999938e-2 | 7.442383471074378e-2 | 7.664065289256196e-2 | 6.618909090909061e-2 | 3.932042435999991e-3 | 3.5072237487603405e-3 | 5.8736895999999044e-3 | 8.341804448644628e-3 | 8.0692826851728e-3 | 5.415841894515349e-3 | 9.133347934161179e-2 | 8.982918615446095e-2 | 7.359240378269587e-2 |
| user_003 | Sitting | 1.446047530061e12 | 0.345482 | -0.689167 | 9.34956 | 0.47437709090909086 | -0.7100688181818182 | 9.447101818181817 | 0.119357812324 | 0.47495115388899994 | 87.41427219360001 | 9.381288885852147 | 9.48648976812681 | 0.12889509090909085 | 2.0901818181818244e-2 | 9.754181818181706e-2 | 8.297463636363635e-2 | 7.790738016528924e-2 | 6.998942148760308e-2 | 1.6613944460462795e-2 | 4.368860033057877e-4 | 9.514406294214657e-3 | 9.741838863456797e-3 | 8.104585913635609e-3 | 5.998356565589729e-3 | 9.870075411797417e-2 | 9.002547369292543e-2 | 7.744905787412606e-2 |
| user_003 | Sitting | 1.446047530126e12 | 0.268841 | -0.804128 | 9.31124 | 0.3594163636363637 | -0.6961341818181818 | 9.433167272727271 | 7.2275483281e-2 | 0.646621840384 | 86.6991903376 | 9.349764043079643 | 9.466339542582668 | 9.05753636363637e-2 | 0.10799381818181819 | 0.12192727272727133 | 8.899164462809915e-2 | 7.062327272727278e-2 | 7.88568595041319e-2 | 8.203896497859516e-3 | 1.1662664765487605e-2 | 1.4866259834710403e-2 | 9.601687393040568e-3 | 6.715567793308799e-3 | 8.253394421036763e-3 | 9.798820027452575e-2 | 8.19485679759494e-2 | 9.08481943741138e-2 |
| user_003 | Sitting | 1.446047530198e12 | 0.422122 | -0.535886 | 9.57948 | 0.43257318181818183 | -0.6403957272727271 | 9.44361818181818 | 0.178186982884 | 0.287173804996 | 91.7664370704 | 9.603738743753913 | 9.47623788966848 | 1.0451181818181832e-2 | 0.10450972727272712 | 0.13586181818181942 | 9.057513223140498e-2 | 7.094005785123968e-2 | 5.7955041322314375e-2 | 1.0922720139669449e-4 | 1.0922283094619801e-2 | 1.8458433639669758e-2 | 1.2908162652194589e-2 | 7.0321907231382375e-3 | 4.926000012021046e-3 | 0.11361409530597244 | 8.385815835765914e-2 | 7.018546866710407e-2 |
| user_003 | Sitting | 1.446047530263e12 | 0.537083 | -0.612526 | 9.46452 | 0.4569586363636363 | -0.6334282727272726 | 9.454069090909092 | 0.28845814888899995 | 0.375188100676 | 89.5771388304 | 9.499514991828004 | 9.48705168646079 | 8.012436363636366e-2 | 2.090227272727263e-2 | 1.0450909090907956e-2 | 6.460612396694213e-2 | 5.2888438016528934e-2 | 4.782082644628134e-2 | 6.4199136481322356e-3 | 4.369050051652852e-4 | 1.0922150082642256e-4 | 7.016752513570993e-3 | 8.101260912214124e-3 | 3.990446146356166e-3 | 8.376605824300791e-2 | 9.000700479526093e-2 | 6.316997820449335e-2 |
| user_003 | Sitting | 1.446047530271e12 | 0.268841 | -0.689167 | 9.46452 | 0.42908936363636363 | -0.636911909090909 | 9.457552727272725 | 7.2275483281e-2 | 0.47495115388899994 | 89.5771388304 | 9.49338535336947 | 9.489527910755147 | 0.16024836363636363 | 5.225509090909097e-2 | 6.967272727274931e-3 | 6.428945454545452e-2 | 5.637208264462811e-2 | 4.6554049586777294e-2 | 2.567953804813223e-2 | 2.730594525917362e-3 | 4.854288925622906e-5 | 6.91415792560931e-3 | 8.331843666842975e-3 | 3.9551422268971105e-3 | 8.315141565607473e-2 | 9.127893331345943e-2 | 6.288992150493679e-2 |
| user_003 | Sitting | 1.446047530287e12 | 0.268841 | -0.459245 | 9.38788 | 0.42560572727272733 | -0.6369118181818182 | 9.440134545454544 | 7.2275483281e-2 | 0.210905970025 | 88.13229089439999 | 9.402950193833103 | 9.472311407267723 | 0.15676472727272733 | 0.17766681818181818 | 5.225454545454511e-2 | 8.614152066115698e-2 | 6.999001652892563e-2 | 3.990347107438035e-2 | 2.4575179716892583e-2 | 3.156549828285124e-2 | 2.730537520661121e-3 | 1.000219283185424e-2 | 1.083956498437566e-2 | 2.5749796255447035e-3 | 0.10001096355827314 | 0.10411323155284183 | 5.074425706959068e-2 |
| user_003 | Sitting | 1.446047530296e12 | 0.422122 | -0.612526 | 9.4262 | 0.42560572727272733 | -0.6334280909090909 | 9.440134545454544 | 0.178186982884 | 0.375188100676 | 88.85324643999999 | 9.455507470440706 | 9.472078704193267 | 3.4837272727273327e-3 | 2.0902090909090898e-2 | 1.3934545454544534e-2 | 8.487472727272723e-2 | 7.125679338842976e-2 | 3.89533884297522e-2 | 1.213635571074422e-5 | 4.368974043719004e-4 | 1.941715570247677e-4 | 9.97571408612321e-3 | 1.087486959480391e-2 | 2.5385724586025533e-3 | 9.987849661525353e-2 | 0.10428264282613818 | 5.0384248119849456e-2 |
| user_003 | Sitting | 1.446047530304e12 | 0.383802 | -0.535886 | 9.54116 | 0.411671090909091 | -0.6264608181818182 | 9.44710181818182 | 0.147303975204 | 0.287173804996 | 91.0337341456 | 9.563901501259828 | 9.477932023232524 | 2.786909090909101e-2 | 9.057481818181823e-2 | 9.405818181818049e-2 | 8.075765289256197e-2 | 7.822404132231409e-2 | 4.750413223140481e-2 | 7.766862280991791e-4 | 8.20379768866943e-3 | 8.846941566941898e-3 | 9.559782529792631e-3 | 1.1603016042987984e-2 | 3.3428398737790897e-3 | 9.777414039403584e-2 | 0.10771729686075483 | 5.781729735796278e-2 |
| user_003 | Sitting | 1.446047530384e12 | 0.537083 | -0.650847 | 9.38788 | 0.47786063636363635 | -0.6299446363636364 | 9.415749090909088 | 0.28845814888899995 | 0.42360181740899994 | 88.13229089439999 | 9.425728134245013 | 9.449721334848572 | 5.922236363636363e-2 | 2.0902363636363552e-2 | 2.7869090909089067e-2 | 8.297439669421487e-2 | 7.505714049586772e-2 | 5.953851239669383e-2 | 3.507288354677685e-3 | 4.3690880558677335e-4 | 7.766862280990708e-4 | 8.225922349926371e-3 | 7.0972933802133705e-3 | 6.396628906987173e-3 | 9.069687067328382e-2 | 8.424543536722551e-2 | 7.997892789345937e-2 |
| user_003 | Sitting | 1.446047530595e12 | 0.345482 | -0.689167 | 9.31124 | 0.4604424545454545 | -0.6787157272727272 | 9.433167272727273 | 0.119357812324 | 0.47495115388899994 | 86.6991903376 | 9.343099020336508 | 9.469798892553213 | 0.1149604545454545 | 1.0451272727272753e-2 | 0.1219272727272731 | 7.505712396694215e-2 | 9.025841322314052e-2 | 6.270545454545452e-2 | 1.3215906109297512e-2 | 1.0922910161983526e-4 | 1.4866259834710837e-2 | 6.889889590413226e-3 | 1.0501938145337343e-2 | 5.904580529526676e-3 | 8.300535880540018e-2 | 0.10247896440410267 | 7.684126840133937e-2 |
| user_003 | Sitting | 1.446047530619e12 | 0.537083 | -0.535886 | 9.50284 | 0.4883117272727272 | -0.6682647272727272 | 9.450585454545454 | 0.28845814888899995 | 0.287173804996 | 90.30396806560002 | 9.533079251715314 | 9.487494546010293 | 4.8771272727272774e-2 | 0.13237872727272726 | 5.2254545454546886e-2 | 6.96732066115703e-2 | 7.252342148760332e-2 | 4.972099173553766e-2 | 2.3786370434380213e-3 | 1.7524127434347104e-2 | 2.7305375206613065e-3 | 6.233436839096926e-3 | 6.4662058201202105e-3 | 3.8801213980466528e-3 | 7.895211738197352e-2 | 8.04127217554549e-2 | 6.2290620466059356e-2 |
| user_003 | Sitting | 1.446047530715e12 | 0.383802 | -0.689167 | 9.34956 | 0.4674097272727273 | -0.6856832727272727 | 9.450585454545452 | 0.147303975204 | 0.47495115388899994 | 87.41427219360001 | 9.382778230497244 | 9.487465577278895 | 8.36077272727273e-2 | 3.4837272727272772e-3 | 0.10102545454545186 | 5.827223966942152e-2 | 5.668853719008264e-2 | 7.632330578512284e-2 | 6.990252059710749e-3 | 1.2136355710743833e-5 | 1.020614246611516e-2 | 4.847752127121718e-3 | 4.378841716747557e-3 | 8.482869897520463e-3 | 6.962580072876517e-2 | 6.617281705313412e-2 | 9.210249669536903e-2 |
| user_003 | Sitting | 1.446047530806e12 | 0.498763 | -0.842448 | 9.38788 | 0.453475090909091 | -0.6961341818181819 | 9.4262 | 0.24876453016900002 | 0.7097186327039999 | 88.13229089439999 | 9.438790921366623 | 9.46375777907497 | 4.528790909090902e-2 | 0.1463138181818181 | 3.8320000000000576e-2 | 7.75906033057851e-2 | 8.835833884297518e-2 | 6.143867768595095e-2 | 2.05099470982644e-3 | 2.1407733390942124e-2 | 1.4684224000000442e-3 | 7.474605228604802e-3 | 9.941506158927872e-3 | 4.905038309842278e-3 | 8.645579927688368e-2 | 9.970710184800215e-2 | 7.003597868126266e-2 |
| user_003 | Sitting | 1.446047531072e12 | 0.537083 | -0.842448 | 9.50284 | 0.43605681818181813 | -0.7031014545454546 | 9.433167272727273 | 0.28845814888899995 | 0.7097186327039999 | 90.30396806560002 | 9.555215583501663 | 9.470374211032171 | 0.10102618181818185 | 0.1393465454545454 | 6.9672727272728e-2 | 8.677482644628101e-2 | 7.474023966942149e-2 | 5.162115702479397e-2 | 1.0206289412760337e-2 | 1.9417459730115685e-2 | 4.854288925619936e-3 | 1.0827435768694223e-2 | 7.281530642746053e-3 | 3.1630105340346233e-3 | 0.10405496513234831 | 8.533188526422027e-2 | 5.624064841406635e-2 |
| user_003 | Sitting | 1.446047531137e12 | 0.575403 | -0.612526 | 9.4262 | 0.46044254545454544 | -0.7135523636363637 | 9.461036363636365 | 0.331088612409 | 0.375188100676 | 88.85324643999999 | 9.463589337724086 | 9.499969737709781 | 0.11496045454545456 | 0.10102636363636364 | 3.4836363636365775e-2 | 9.374212396694216e-2 | 6.428938016528928e-2 | 6.618909090909159e-2 | 1.3215906109297524e-2 | 1.0206326149586777e-2 | 1.2135722314051078e-3 | 1.0754603411913602e-2 | 5.863855093449286e-3 | 6.155017708189375e-3 | 0.10370440401407069 | 7.657581271817679e-2 | 7.845392092298112e-2 |
| user_003 | Sitting | 1.446047531291e12 | 0.383802 | -0.727487 | 9.34956 | 0.4395404545454546 | -0.6612975454545454 | 9.436650909090908 | 0.147303975204 | 0.529237335169 | 87.41427219360001 | 9.385670647533559 | 9.470928100179533 | 5.573845454545462e-2 | 6.618945454545455e-2 | 8.709090909090733e-2 | 8.139105785123965e-2 | 7.442380165289253e-2 | 5.795504132231389e-2 | 3.106775315115711e-3 | 4.381043893024794e-3 | 7.584826446280685e-3 | 9.328115414305027e-3 | 7.752650081687448e-3 | 4.160346258752776e-3 | 9.658216923586375e-2 | 8.804913447437998e-2 | 6.450074618756574e-2 |
| user_003 | Sitting | 1.446047531299e12 | 0.460442 | -0.612526 | 9.50284 | 0.4465077272727273 | -0.6717485454545454 | 9.450585454545454 | 0.21200683536400003 | 0.375188100676 | 90.30396806560002 | 9.53368569870226 | 9.485749208759716 | 1.3934272727272712e-2 | 5.922254545454542e-2 | 5.2254545454546886e-2 | 7.790738016528921e-2 | 6.745647933884294e-2 | 5.193785123966954e-2 | 1.941639564380161e-4 | 3.507309890115698e-3 | 2.7305375206613065e-3 | 9.097534271949656e-3 | 6.393425574300523e-3 | 3.133222851990991e-3 | 9.538099533947869e-2 | 7.995889928144662e-2 | 5.597519854356026e-2 |
| user_003 | Sitting | 1.446047531379e12 | 0.383802 | -0.842448 | 9.54116 | 0.48831172727272726 | -0.7623236363636363 | 9.478454545454547 | 0.147303975204 | 0.7097186327039999 | 91.0337341456 | 9.585966657229097 | 9.522192588486204 | 0.10450972727272728 | 8.012436363636366e-2 | 6.270545454545307e-2 | 6.2389223140495866e-2 | 5.700529752066119e-2 | 7.093950413223092e-2 | 1.0922283094619836e-2 | 6.4199136481322356e-3 | 3.93197402975188e-3 | 5.983005936812919e-3 | 5.2691818403899394e-3 | 6.601832938842907e-3 | 7.734989293342893e-2 | 7.258913031845704e-2 | 8.125166422198937e-2 |
| user_003 | Sitting | 1.446047531436e12 | 0.575403 | -0.650847 | 9.54116 | 0.45347509090909094 | -0.710068909090909 | 9.488905454545455 | 0.331088612409 | 0.42360181740899994 | 91.0337341456 | 9.580627566888193 | 9.527012641437604 | 0.12192790909090906 | 5.9221909090909075e-2 | 5.225454545454511e-2 | 7.379029752066112e-2 | 7.537373553719011e-2 | 6.58723966942144e-2 | 1.4866415015280984e-2 | 3.507234516371899e-3 | 2.730537520661121e-3 | 7.448131895531173e-3 | 8.757696907326832e-3 | 5.891341559729494e-3 | 8.63025601910579e-2 | 9.358256732600806e-2 | 7.675507513988566e-2 |
| user_003 | Sitting | 1.446047531461e12 | 0.460442 | -0.689167 | 9.31124 | 0.4360566363636364 | -0.6821996363636363 | 9.471487272727273 | 0.21200683536400003 | 0.47495115388899994 | 86.6991903376 | 9.348055858137188 | 9.5065762271645 | 2.438536363636362e-2 | 6.967363636363633e-3 | 0.16024727272727368 | 6.365588429752062e-2 | 6.84063140495868e-2 | 7.252297520661166e-2 | 5.946459596776853e-4 | 4.854415604132226e-5 | 2.567918841652923e-2 | 6.552275419301271e-3 | 7.945683151519166e-3 | 7.061887139293805e-3 | 8.094612664792103e-2 | 8.913856152933569e-2 | 8.403503518946015e-2 |
| user_003 | Sitting | 1.446047531541e12 | 0.422122 | -0.727487 | 9.6178 | 0.42908936363636363 | -0.6578139090909091 | 9.450585454545454 | 0.178186982884 | 0.529237335169 | 92.50207684000002 | 9.654506779636804 | 9.483805630602435 | 6.967363636363633e-3 | 6.96730909090909e-2 | 0.16721454545454684 | 5.890554545454545e-2 | 5.890566942148759e-2 | 5.732165289256211e-2 | 4.854415604132226e-5 | 4.854339596826446e-3 | 2.7960704211570712e-2 | 5.5405918919316285e-3 | 5.697279233977457e-3 | 6.384493184673259e-3 | 7.44351522597464e-2 | 7.548032348882361e-2 | 7.990302362660164e-2 |
| user_003 | Sitting | 1.44604753193e12 | 0.460442 | -0.880768 | 9.46452 | 0.4534750909090908 | -0.7170360909090907 | 9.422716363636363 | 0.21200683536400003 | 0.775752269824 | 89.5771388304 | 9.516559143702517 | 9.461390118682749 | 6.96690909090919e-3 | 0.1637319090909093 | 4.180363636363715e-2 | 3.0719553719008252e-2 | 7.727383471074388e-2 | 5.320462809917359e-2 | 4.853782228099312e-5 | 2.6808138054553784e-2 | 1.7475440132232066e-3 | 1.446381519275732e-3 | 9.485840937113464e-3 | 5.361782767843718e-3 | 3.8031322870441045e-2 | 9.7395281903763e-2 | 7.322419523520704e-2 |
| user_003 | Sitting | 1.446047531939e12 | 0.460442 | -0.612526 | 9.54116 | 0.453475090909091 | -0.6961340909090908 | 9.433167272727273 | 0.21200683536400003 | 0.375188100676 | 91.0337341456 | 9.571882212064668 | 9.470200725599252 | 6.966909090909024e-3 | 8.360809090909083e-2 | 0.1079927272727268 | 3.0719553719008252e-2 | 7.537367768595048e-2 | 6.2072066115702415e-2 | 4.85378222809908e-5 | 6.990312865462796e-3 | 1.166242914380155e-2 | 1.4463815192757316e-3 | 9.128387370664928e-3 | 6.4120743717505455e-3 | 3.803132287044104e-2 | 9.554259453597086e-2 | 8.007542926360461e-2 |
| user_003 | Sitting | 1.446047531971e12 | 0.537083 | -0.689167 | 9.46452 | 0.4743770909090909 | -0.7065851818181819 | 9.44710181818182 | 0.28845814888899995 | 0.47495115388899994 | 89.5771388304 | 9.504764496460604 | 9.486084813100682 | 6.270590909090906e-2 | 1.7418181818181888e-2 | 1.741818181818111e-2 | 5.352172727272724e-2 | 7.062314876033061e-2 | 7.790677685950423e-2 | 3.932031034917352e-3 | 3.033930578512421e-4 | 3.0339305785121503e-4 | 5.135698914038311e-3 | 8.434428638700983e-3 | 9.78801167002251e-3 | 7.16637908154342e-2 | 9.18391454593355e-2 | 9.893438062687061e-2 |
| user_003 | Sitting | 1.446047531995e12 | 0.422122 | -0.650847 | 9.38788 | 0.474377 | -0.6717485454545454 | 9.447101818181817 | 0.178186982884 | 0.42360181740899994 | 88.13229089439999 | 9.419876840739107 | 9.484029092844334 | 5.2254999999999996e-2 | 2.0901545454545478e-2 | 5.9221818181818264e-2 | 6.333926446280991e-2 | 8.329109917355373e-2 | 7.695669421487607e-2 | 2.7305850249999997e-3 | 4.368746023884307e-4 | 3.5072237487603405e-3 | 6.096636627168289e-3 | 1.3573471843867018e-2 | 9.839864301727981e-3 | 7.808096200206738e-2 | 0.11650524384707762 | 9.919609015343286e-2 |
| user_003 | Sitting | 1.446047532035e12 | 0.537083 | -0.650847 | 9.57948 | 0.48482800000000004 | -0.6403957272727273 | 9.443618181818183 | 0.28845814888899995 | 0.42360181740899994 | 91.7664370704 | 9.616574080029645 | 9.478619727881672 | 5.225499999999994e-2 | 1.0451272727272642e-2 | 0.13586181818181764 | 6.333933057851242e-2 | 8.044087603305784e-2 | 7.600661157024807e-2 | 2.7305850249999936e-3 | 1.0922910161983294e-4 | 1.8458433639669276e-2 | 4.435117760483097e-3 | 1.3700343289062356e-2 | 1.0200626228700162e-2 | 6.659667980074606e-2 | 0.11704846555620606 | 0.10099814963008066 |
| user_003 | Sitting | 1.446047532043e12 | 0.460442 | -0.612526 | 9.46452 | 0.48831163636363634 | -0.6369119999999999 | 9.4262 | 0.21200683536400003 | 0.375188100676 | 89.5771388304 | 9.495490180419335 | 9.461188754571895 | 2.786963636363632e-2 | 2.4385999999999908e-2 | 3.8320000000000576e-2 | 6.080577685950414e-2 | 7.822407438016526e-2 | 6.048859504132264e-2 | 7.767166310413198e-4 | 5.946769959999955e-4 | 1.4684224000000442e-3 | 4.223291479851993e-3 | 1.3538169133596541e-2 | 6.362428235011264e-3 | 6.498685620840566e-2 | 0.11635363824821526 | 7.976483081541177e-2 |
| user_003 | Sitting | 1.446047532206e12 | 0.498763 | -0.727487 | 9.4262 | 0.463926 | -0.6403955454545455 | 9.447101818181817 | 0.24876453016900002 | 0.529237335169 | 88.85324643999999 | 9.467378111459265 | 9.480533747276406 | 3.483700000000001e-2 | 8.709145454545453e-2 | 2.090181818181769e-2 | 4.655442148760331e-2 | 5.3205074380165314e-2 | 5.162115702479397e-2 | 1.2136165690000004e-3 | 7.584921454842972e-3 | 4.368860033057645e-4 | 3.293241529291509e-3 | 3.358336182575511e-3 | 4.283909976859568e-3 | 5.738677137887711e-2 | 5.795115341885363e-2 | 6.545158498355534e-2 |
| user_003 | Sitting | 1.44604753231e12 | 0.15388 | -0.459245 | 9.50284 | 0.4290894545454545 | -0.7031014545454547 | 9.394847272727272 | 2.3679054399999996e-2 | 0.210905970025 | 90.30396806560002 | 9.515174884889138 | 9.432383116562805 | 0.27520945454545453 | 0.24385645454545468 | 0.10799272727272857 | 8.455802479338846e-2 | 0.10482652892561983 | 7.505652892561993e-2 | 7.57402438712066e-2 | 5.9465970423479404e-2 | 1.1662429143801934e-2 | 1.3054925718858756e-2 | 1.5519611574661167e-2 | 7.93565914590532e-3 | 0.11425815383970965 | 0.12457773306117416 | 8.908231668465588e-2 |
| user_003 | Sitting | 1.446047532318e12 | 0.345482 | -0.689167 | 9.50284 | 0.4360568181818181 | -0.6961341818181819 | 9.412265454545455 | 0.119357812324 | 0.47495115388899994 | 90.30396806560002 | 9.534058791082265 | 9.449429714227982 | 9.057481818181806e-2 | 6.9671818181819e-3 | 9.057454545454569e-2 | 7.600719008264466e-2 | 9.405885950413224e-2 | 6.99894214876034e-2 | 8.2037976886694e-3 | 4.8541622487604446e-5 | 8.203748284297563e-3 | 1.0701660603646882e-2 | 1.4094201200951173e-2 | 6.735325884297508e-3 | 0.10344883084717238 | 0.11871900101058454 | 8.206903121334812e-2 |
| user_003 | Sitting | 1.446047532358e12 | 0.575403 | -0.612526 | 9.46452 | 0.4256057272727272 | -0.6612975454545453 | 9.440134545454544 | 0.331088612409 | 0.375188100676 | 89.5771388304 | 9.501758550051933 | 9.473956697352563 | 0.14979727272727278 | 4.877154545454532e-2 | 2.4385454545456042e-2 | 7.98073966942149e-2 | 6.935655371900826e-2 | 7.252297520661183e-2 | 2.243922291652894e-2 | 2.37866364602478e-3 | 5.946503933885027e-4 | 1.2078515765728777e-2 | 9.352380409850493e-3 | 7.412719838918071e-3 | 0.10990230100288519 | 9.670770605205406e-2 | 8.609715348905601e-2 |
| user_003 | Sitting | 1.446047532489e12 | 0.422122 | -0.650847 | 9.57948 | 0.4499914545454545 | -0.6787159090909092 | 9.509807272727274 | 0.178186982884 | 0.42360181740899994 | 91.7664370704 | 9.610838978501981 | 9.545113145434641 | 2.7869454545454475e-2 | 2.7868909090909222e-2 | 6.967272727272622e-2 | 7.854063636363634e-2 | 5.098814049586773e-2 | 5.890512396694237e-2 | 7.767064966611531e-4 | 7.766760939173626e-4 | 4.854288925619688e-3 | 7.005696718364385e-3 | 2.98873906277911e-3 | 4.942548724267481e-3 | 8.370004013358885e-2 | 5.4669361280145844e-2 | 7.030326254355114e-2 |
| user_003 | Sitting | 1.446047532674e12 | 0.422122 | -0.727487 | 9.34956 | 0.44999127272727274 | -0.6578138181818182 | 9.433167272727271 | 0.178186982884 | 0.529237335169 | 87.41427219360001 | 9.387315724511081 | 9.467545225051337 | 2.7869272727272743e-2 | 6.967318181818183e-2 | 8.360727272727075e-2 | 4.845439669421489e-2 | 7.917409917355372e-2 | 5.700495867768525e-2 | 7.766963623471083e-4 | 4.854352264669423e-3 | 6.990176052892232e-3 | 3.6021380415386926e-3 | 1.0273590561117955e-2 | 4.172481981066776e-3 | 6.0017814368224814e-2 | 0.10135872217583426 | 6.459475196226684e-2 |
| user_003 | Sitting | 1.446047532748e12 | 0.422122 | -0.535886 | 9.4262 | 0.443024 | -0.6856832727272728 | 9.419232727272727 | 0.178186982884 | 0.287173804996 | 88.85324643999999 | 9.450852195854086 | 9.45518690012993 | 2.0901999999999976e-2 | 0.14979727272727283 | 6.967272727273155e-3 | 5.510514049586778e-2 | 7.15733223140496e-2 | 6.618909090909061e-2 | 4.36893603999999e-4 | 2.243922291652896e-2 | 4.854288925620431e-5 | 4.918344246375656e-3 | 8.475250983612322e-3 | 6.439655558827918e-3 | 7.013090792493461e-2 | 9.206112634338297e-2 | 8.024746450092936e-2 |
| user_003 | Sitting | 1.44604753278e12 | 0.460442 | -0.535886 | 9.34956 | 0.44650763636363633 | -0.6264610909090907 | 9.436650909090908 | 0.21200683536400003 | 0.287173804996 | 87.41427219360001 | 9.376217405433815 | 9.468417061610051 | 1.3934363636363689e-2 | 9.057509090909077e-2 | 8.709090909090733e-2 | 5.098812396694215e-2 | 6.08056611570248e-2 | 5.573818181818201e-2 | 1.941664899504147e-4 | 8.203847093190058e-3 | 7.584826446280685e-3 | 4.283975561659653e-3 | 6.140751440224645e-3 | 4.9668201688956e-3 | 6.545208599929915e-2 | 7.836294685771232e-2 | 7.047567075874908e-2 |
| user_003 | Sitting | 1.446047532812e12 | 0.460442 | -0.727487 | 9.38788 | 0.4116710000000001 | -0.636912 | 9.450585454545456 | 0.21200683536400003 | 0.529237335169 | 88.13229089439999 | 9.427276121178004 | 9.48151896497497 | 4.87709999999999e-2 | 9.057499999999996e-2 | 6.270545454545662e-2 | 5.4155008264462796e-2 | 6.492275206611571e-2 | 6.492231404958705e-2 | 2.37861044099999e-3 | 8.203830624999993e-3 | 3.931974029752326e-3 | 5.554918965370397e-3 | 5.702761556047335e-3 | 5.493069218332115e-3 | 7.453132875087091e-2 | 7.551663098978485e-2 | 7.41152428204355e-2 |
| user_003 | Sitting | 1.446047533573e12 | 0.422122 | -0.612526 | 9.50284 | 0.4465077272727273 | -0.678716 | 9.440134545454546 | 0.178186982884 | 0.375188100676 | 90.30396806560002 | 9.531911830748331 | 9.475293232841638 | 2.438572727272731e-2 | 6.618999999999997e-2 | 6.270545454545484e-2 | 4.3070669421487594e-2 | 4.2120636363636346e-2 | 2.7552396694215112e-2 | 5.946636946198365e-4 | 4.381116099999996e-3 | 3.931974029752103e-3 | 2.6566612249744517e-3 | 3.412407228598795e-3 | 1.1242091852742531e-3 | 5.1542809634074586e-2 | 5.8415813172451814e-2 | 3.352922882015411e-2 |
| user_003 | Sitting | 1.446047533832e12 | 0.498763 | -0.650847 | 9.46452 | 0.4430240909090909 | -0.6682650909090909 | 9.450585454545454 | 0.24876453016900002 | 0.42360181740899994 | 89.5771388304 | 9.499973956699987 | 9.48470703154306 | 5.573890909090912e-2 | 1.7418090909090966e-2 | 1.393454545454631e-2 | 3.6103363636363635e-2 | 2.7235809917355372e-2 | 2.4068760330578375e-2 | 3.106825986644631e-3 | 3.0338989091735735e-4 | 1.9417155702481723e-4 | 1.815969944335836e-3 | 1.2290372737678431e-3 | 9.92922734785878e-4 | 4.261419885831289e-2 | 3.50576278970475e-2 | 3.151067652059977e-2 |
| user_003 | Sitting | 1.446047534933e12 | 0.460442 | -0.650847 | 9.4262 | 0.45695872727272724 | -0.6543305454545453 | 9.464519999999998 | 0.21200683536400003 | 0.42360181740899994 | 88.85324643999999 | 9.459854919224343 | 9.498204811428216 | 3.4832727272727793e-3 | 3.4835454545453226e-3 | 3.83199999999988e-2 | 3.166985123966942e-2 | 1.3617900826446205e-2 | 1.5201322314049552e-2 | 1.2133188892562345e-5 | 1.2135088933883378e-5 | 1.468422399999908e-3 | 1.4739708828970693e-3 | 3.4973676497971284e-4 | 4.83222397595789e-4 | 3.8392328438075614e-2 | 1.8701250358725025e-2 | 2.1982320114032297e-2 |
| user_003 | Sitting | 1.446047535192e12 | 0.460442 | -0.650847 | 9.46452 | 0.44302390909090905 | -0.6543305454545454 | 9.457552727272725 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.49057392074552 | 1.7418090909090966e-2 | 3.4835454545454336e-3 | 6.967272727274931e-3 | 2.2802198347107446e-2 | 1.3301223140495805e-2 | 2.21685950413219e-2 | 3.0338989091735735e-4 | 1.2135088933884152e-5 | 4.854288925622906e-5 | 8.804058292652138e-4 | 3.971768098181803e-4 | 7.435888036062852e-4 | 2.967163341080524e-2 | 1.992929526647092e-2 | 2.7268824756602278e-2 |
| user_003 | Sitting | 1.446047535256e12 | 0.460442 | -0.612526 | 9.38788 | 0.43954018181818183 | -0.6508468181818181 | 9.450585454545454 | 0.21200683536400003 | 0.375188100676 | 88.13229089439999 | 9.419102177513523 | 9.483221940819478 | 2.0901818181818188e-2 | 3.8320818181818095e-2 | 6.270545454545484e-2 | 2.1218677685950416e-2 | 1.6151553719008196e-2 | 2.786909090909052e-2 | 4.368860033057854e-4 | 1.4684851061239604e-3 | 3.931974029752103e-3 | 7.866259928444773e-4 | 5.262625810578491e-4 | 1.1010409881292036e-3 | 2.8046853528417005e-2 | 2.2940413707207834e-2 | 3.3181937678942196e-2 |
| user_003 | Sitting | 1.44604753571e12 | 0.422122 | -0.689167 | 9.4262 | 0.44302390909090905 | -0.6682650909090909 | 9.433167272727273 | 0.178186982884 | 0.47495115388899994 | 88.85324643999999 | 9.460781393562215 | 9.467261238993999 | 2.0901909090909054e-2 | 2.0901909090909054e-2 | 6.967272727273155e-3 | 2.470223966942148e-2 | 2.1852033057851227e-2 | 2.818578512396722e-2 | 4.368898036446266e-4 | 4.368898036446266e-4 | 4.854288925620431e-5 | 8.627507025326816e-4 | 6.299638713298256e-4 | 1.1837845493613984e-3 | 2.9372618244424203e-2 | 2.5099081085366962e-2 | 3.440617022223483e-2 |
| user_003 | Sitting | 1.446047537393e12 | 0.460442 | -0.727487 | 9.46452 | 0.4604421818181818 | -0.6612978181818181 | 9.450585454545454 | 0.21200683536400003 | 0.529237335169 | 89.5771388304 | 9.503598423804164 | 9.484951057799584 | 1.8181818178808484e-7 | 6.61891818181819e-2 | 1.393454545454631e-2 | 1.8051826446281e-2 | 1.900171900826445e-2 | 2.723570247933922e-2 | 3.3057851228725065e-14 | 4.381007789760341e-3 | 1.9417155702481723e-4 | 6.277611767535688e-4 | 7.5904256007964e-4 | 9.708577851239973e-4 | 2.5055162676653465e-2 | 2.7550727033594596e-2 | 3.1158590871924827e-2 |
| user_003 | Sitting | 1.446047537522e12 | 0.460442 | -0.650847 | 9.46452 | 0.4604421818181818 | -0.6682650909090908 | 9.457552727272725 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.492399032727578 | 1.8181818178808484e-7 | 1.7418090909090855e-2 | 6.967272727274931e-3 | 1.5835033057851236e-2 | 2.5018925619834695e-2 | 2.533553719008339e-2 | 3.3057851228725065e-14 | 3.033898909173535e-4 | 4.854288925622906e-5 | 6.001807958069121e-4 | 1.0999468960676186e-3 | 9.17901905935422e-4 | 2.449858762881877e-2 | 3.316544732198887e-2 | 3.0296895978555657e-2 |
| user_003 | Sitting | 1.446047537717e12 | 0.460442 | -0.650847 | 9.46452 | 0.4499910909090909 | -0.668265 | 9.46452 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.498850395313655 | 1.0450909090909122e-2 | 1.7418000000000045e-2 | 0.0 | 1.330133057851239e-2 | 3.293635537190081e-2 | 1.7418181818182565e-2 | 1.0922150082644693e-4 | 3.0338672400000155e-4 | 0.0 | 4.5233693828625117e-4 | 1.579867727745303e-3 | 5.019776048084481e-4 | 2.126821427121354e-2 | 3.974754995902644e-2 | 2.2404856723675967e-2 |
| user_003 | Sitting | 1.446047538279e12 | 0.383802 | -0.689167 | 9.4262 | 0.495279 | -0.6787159090909091 | 9.48542181818182 | 0.147303975204 | 0.47495115388899994 | 88.85324643999999 | 9.45914909329021 | 9.523077168296753 | 0.11147700000000005 | 1.045109090909091e-2 | 5.922181818182004e-2 | 6.460606611570249e-2 | 4.148727272727273e-2 | 5.320462809917343e-2 | 1.2427121529000012e-2 | 1.0922530119008267e-4 | 3.507223748760551e-3 | 7.634576459154021e-3 | 2.7261733594996255e-3 | 4.3048716790382874e-3 | 8.737606342216397e-2 | 5.221277008069602e-2 | 6.561152093221348e-2 |
| user_003 | Sitting | 1.446047538335e12 | 0.498763 | -0.842448 | 9.50284 | 0.46740972727272734 | -0.7135525454545454 | 9.481938181818181 | 0.24876453016900002 | 0.7097186327039999 | 90.30396806560002 | 9.553138292125421 | 9.52063797478479 | 3.1353272727272674e-2 | 0.1288954545454546 | 2.0901818181819465e-2 | 4.497107438016528e-2 | 5.0988008264462814e-2 | 4.877090909090934e-2 | 9.830277107107405e-4 | 1.661403820247935e-2 | 4.368860033058388e-4 | 3.1950531367272734e-3 | 4.3832547066378685e-3 | 3.2898839945905613e-3 | 5.652480107640604e-2 | 6.620615308744247e-2 | 5.735751035906773e-2 |
| user_003 | Sitting | 1.446047538392e12 | 0.460442 | -0.650847 | 9.46452 | 0.4360568181818181 | -0.6856831818181818 | 9.436650909090908 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.472047228613263 | 2.4385181818181945e-2 | 3.483618181818182e-2 | 2.786909090909262e-2 | 5.2888305785123955e-2 | 6.428925619834713e-2 | 6.048859504132296e-2 | 5.946370923057913e-4 | 1.2135595636694218e-3 | 7.766862280992689e-4 | 3.5889070755289252e-3 | 5.640976702833963e-3 | 4.272877502028614e-3 | 5.990748764160391e-2 | 7.510643582832274e-2 | 6.536725099029798e-2 |
| user_003 | Sitting | 1.446047538432e12 | 0.422122 | -0.497565 | 9.27292 | 0.40818745454545446 | -0.6682648181818182 | 9.412265454545455 | 0.178186982884 | 0.24757092922499999 | 85.98704532639998 | 9.295848709962366 | 9.445277627661863 | 1.3934545454545533e-2 | 0.17069981818181817 | 0.139345454545456 | 5.003804132231404e-2 | 6.998995867768594e-2 | 6.1121983471075066e-2 | 1.9417155702479557e-4 | 2.9138427927305782e-2 | 1.9417155702479743e-2 | 3.4112903343568716e-3 | 7.077453178788129e-3 | 5.137823528775436e-3 | 5.840625252793464e-2 | 8.412760057667239e-2 | 7.167861277100329e-2 |
| user_003 | Sitting | 1.446047538505e12 | 0.345482 | -0.727487 | 9.4262 | 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 0.119357812324 | 0.529237335169 | 88.85324643999999 | 9.460541294634941 | 9.477304790774603 | 7.664018181818172e-2 | 1.3934545454545533e-2 | 1.393454545454631e-2 | 6.397261983471073e-2 | 6.745627272727277e-2 | 8.582413223140571e-2 | 5.873717469123952e-3 | 1.9417155702479557e-4 | 1.9417155702481723e-4 | 7.071922085413219e-3 | 8.233656195504889e-3 | 8.81605063741558e-3 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 |
| user_003 | Sitting | 1.44604753857e12 | 0.498763 | -0.689167 | 9.46452 | 0.46044245454545457 | -0.6264609999999999 | 9.412265454545452 | 0.24876453016900002 | 0.47495115388899994 | 89.5771388304 | 9.502676176449349 | 9.44500141395286 | 3.832054545454544e-2 | 6.270600000000004e-2 | 5.225454545454866e-2 | 5.542180991735537e-2 | 7.790726446280988e-2 | 6.048859504132215e-2 | 1.4684642039338833e-3 | 3.932042436000005e-3 | 2.730537520661492e-3 | 4.3766354520864e-3 | 9.210033615250928e-3 | 5.885825322314023e-3 | 6.615614447718669e-2 | 9.596892004837257e-2 | 7.671913270048106e-2 |
| user_003 | Sitting | 1.446047538748e12 | 0.383802 | -0.689167 | 9.46452 | 0.453475 | -0.6543303636363637 | 9.47497090909091 | 0.147303975204 | 0.47495115388899994 | 89.5771388304 | 9.49733615070526 | 9.50865397554464 | 6.967300000000004e-2 | 3.483663636363632e-2 | 1.0450909090909732e-2 | 4.940473553719011e-2 | 3.261980165289257e-2 | 6.2072066115702734e-2 | 4.854326929000005e-3 | 1.2135912331322283e-3 | 1.092215008264597e-4 | 3.706951965102183e-3 | 1.7023409238046577e-3 | 6.46723674590533e-3 | 6.0884743286821724e-2 | 4.125943436118166e-2 | 8.041913171568896e-2 |
| user_003 | Sitting | 1.446047538829e12 | 0.307161 | -0.574206 | 9.46452 | 0.4360567272727273 | -0.647362909090909 | 9.450585454545454 | 9.434787992100001e-2 | 0.329712530436 | 89.5771388304 | 9.48689618583217 | 9.483547031497928 | 0.1288957272727273 | 7.3156909090909e-2 | 1.393454545454631e-2 | 7.885733884297523e-2 | 5.0354834710743784e-2 | 5.542148760330628e-2 | 1.6614108509165296e-2 | 5.351933347735523e-3 | 1.9417155702481723e-4 | 9.524480510567998e-3 | 3.822799975695719e-3 | 5.989530585725073e-3 | 9.759344501844372e-2 | 6.182879568369191e-2 | 7.739205763981905e-2 |
| user_003 | Sitting | 1.446047538926e12 | 0.383802 | -0.612526 | 9.50284 | 0.42908945454545455 | -0.6299446363636363 | 9.447101818181817 | 0.147303975204 | 0.375188100676 | 90.30396806560002 | 9.53029171334645 | 9.478177724029353 | 4.5287454545454575e-2 | 1.7418636363636275e-2 | 5.573818181818346e-2 | 4.592104132231405e-2 | 4.46544297520661e-2 | 5.06710743801663e-2 | 2.050953539206614e-3 | 3.03408892768592e-4 | 3.1067449123968775e-3 | 2.812216471304282e-3 | 4.6502598296844465e-3 | 3.5855543200602083e-3 | 5.3030335387439155e-2 | 6.81928136219972e-2 | 5.987949832839457e-2 |
| user_003 | Sitting | 1.44604753908e12 | 0.383802 | -0.650847 | 9.31124 | 0.48482818181818177 | -0.6369120909090908 | 9.422716363636363 | 0.147303975204 | 0.42360181740899994 | 86.6991903376 | 9.34184650538709 | 9.457423948328541 | 0.10102618181818179 | 1.3934909090909109e-2 | 0.11147636363636337 | 9.374225619834707e-2 | 5.003799173553718e-2 | 5.5421487603306445e-2 | 1.0206289412760326e-2 | 1.9418169137190133e-4 | 1.2426979649586719e-2 | 1.1577654018679938e-2 | 3.729024227525918e-3 | 4.711970000300567e-3 | 0.10759950752061989 | 6.106573693591127e-2 | 6.864379069005853e-2 |
| user_003 | Sitting | 1.446047539104e12 | 0.345482 | -0.765807 | 9.38788 | 0.4465079090909091 | -0.6508467272727272 | 9.412265454545455 | 0.119357812324 | 0.586460361249 | 88.13229089439999 | 9.425397024421464 | 9.446224382937823 | 0.10102590909090908 | 0.11496027272727283 | 2.4385454545456042e-2 | 0.10292638842975207 | 5.573845454545454e-2 | 5.288793388429835e-2 | 1.0206234307644627e-2 | 1.3215864305528948e-2 | 5.946503933885027e-4 | 1.2494465863184822e-2 | 4.593966684227649e-3 | 4.7384479398948735e-3 | 0.11177864672281922 | 6.777880704340884e-2 | 6.883638529073759e-2 |
| user_003 | Sitting | 1.446047539185e12 | 0.422122 | -0.574206 | 9.46452 | 0.40470390909090903 | -0.6682649090909091 | 9.461036363636362 | 0.178186982884 | 0.329712530436 | 89.5771388304 | 9.491313836541282 | 9.493686059905166 | 1.7418090909090966e-2 | 9.405890909090908e-2 | 3.483636363638354e-3 | 5.0987925619834715e-2 | 6.112228925619836e-2 | 5.288793388429818e-2 | 3.0338989091735735e-4 | 8.8470783793719e-3 | 1.2135722314063454e-5 | 4.041231345951162e-3 | 5.376178189951167e-3 | 7.048648169496783e-3 | 6.357067992361858e-2 | 7.332242624157473e-2 | 8.395622769930045e-2 |
| user_003 | Sitting | 1.44604753925e12 | 0.575403 | -0.650847 | 9.54116 | 0.4290894545454545 | -0.6473630909090909 | 9.46452 | 0.331088612409 | 0.42360181740899994 | 91.0337341456 | 9.580627566888193 | 9.497061195942141 | 0.1463135454545455 | 3.48390909090901e-3 | 7.663999999999938e-2 | 6.618947107438017e-2 | 5.1938214876033055e-2 | 6.428892561983429e-2 | 2.1407653583479354e-2 | 1.2137622553718444e-5 | 5.8736895999999044e-3 | 7.6180346566506385e-3 | 5.266982370293012e-3 | 6.86109609737033e-3 | 8.728135343044721e-2 | 7.257397860316749e-2 | 8.283173363735864e-2 |
| user_003 | Sitting | 1.446047539266e12 | 0.422122 | -0.574206 | 9.31124 | 0.4290894545454545 | -0.636912090909091 | 9.43665090909091 | 0.178186982884 | 0.329712530436 | 86.6991903376 | 9.33847363603496 | 9.468555330556804 | 6.967454545454499e-3 | 6.270609090909096e-2 | 0.12541090909090968 | 6.175579338842974e-2 | 5.542194214876033e-2 | 7.378975206611539e-2 | 4.854542284297456e-5 | 3.93205383709918e-3 | 1.5727896119008412e-2 | 7.3532575639436515e-3 | 5.581416137058603e-3 | 8.063635853944357e-3 | 8.575113739154515e-2 | 7.470887589208261e-2 | 8.979774971537069e-2 |
| user_003 | Sitting | 1.446047539347e12 | 0.422122 | -0.650847 | 9.34956 | 0.4708934545454546 | -0.636912090909091 | 9.419232727272727 | 0.178186982884 | 0.42360181740899994 | 87.41427219360001 | 9.381687534441392 | 9.453234713500297 | 4.877145454545462e-2 | 1.3934909090908998e-2 | 6.967272727272622e-2 | 8.297461157024794e-2 | 5.320521487603303e-2 | 7.157289256198367e-2 | 2.378654778479346e-3 | 1.9418169137189824e-4 | 4.854288925619688e-3 | 8.887917710833963e-3 | 4.8455447108512375e-3 | 6.067861157024843e-3 | 9.427575356810447e-2 | 6.960994692464029e-2 | 7.789647717981117e-2 |
| user_003 | Sitting | 1.446047539557e12 | 0.498763 | -0.727487 | 9.50284 | 0.40818745454545463 | -0.6752319999999999 | 9.443618181818183 | 0.24876453016900002 | 0.529237335169 | 90.30396806560002 | 9.543687438874873 | 9.477360402629962 | 9.057554545454538e-2 | 5.225500000000005e-2 | 5.9221818181818264e-2 | 7.949081818181819e-2 | 6.618962809917359e-2 | 6.967272727272751e-2 | 8.203929434388416e-3 | 2.7305850250000053e-3 | 3.5072237487603405e-3 | 1.009374481075357e-2 | 6.2014605697948925e-3 | 7.4954634001502905e-3 | 0.10046763066158956 | 7.874935282143525e-2 | 8.657634434503625e-2 |
| user_003 | Sitting | 1.446047539711e12 | 0.422122 | -0.727487 | 9.50284 | 0.46740972727272734 | -0.6891668181818182 | 9.478454545454547 | 0.178186982884 | 0.529237335169 | 90.30396806560002 | 9.539989118633889 | 9.515677464366703 | 4.528772727272734e-2 | 3.832018181818175e-2 | 2.4385454545454266e-2 | 7.980754545454548e-2 | 5.067146280991737e-2 | 3.832000000000025e-2 | 2.0509782415289316e-3 | 1.4684363345785075e-3 | 5.946503933884161e-4 | 1.0359656993277988e-2 | 3.554719647222392e-3 | 2.4591386398197055e-3 | 0.10178240021377953 | 5.962146968351579e-2 | 4.9589702961599855e-2 |
| user_003 | Sitting | 1.446047539776e12 | 0.498763 | -0.612526 | 9.54116 | 0.45347509090909094 | -0.6508465454545453 | 9.436650909090908 | 0.24876453016900002 | 0.375188100676 | 91.0337341456 | 9.573802106605557 | 9.470590736802814 | 4.528790909090907e-2 | 3.832054545454533e-2 | 0.104509090909092 | 5.54219917355372e-2 | 6.048900000000001e-2 | 5.827173553719059e-2 | 2.0509947098264446e-3 | 1.4684642039338746e-3 | 1.0922150082644855e-2 | 4.222202858008266e-3 | 7.463574826676937e-3 | 4.357827558226931e-3 | 6.497847996073983e-2 | 8.63919835787843e-2 | 6.601384368620669e-2 |
| user_003 | Sitting | 1.446047539792e12 | 0.537083 | -0.727487 | 9.4262 | 0.45695872727272735 | -0.6612975454545453 | 9.422716363636363 | 0.28845814888899995 | 0.529237335169 | 88.85324643999999 | 9.469474215818849 | 9.457644533197595 | 8.012427272727263e-2 | 6.618945454545466e-2 | 3.4836363636365775e-3 | 6.017241322314049e-2 | 6.333923140495869e-2 | 5.985520661157053e-2 | 6.419899080074364e-3 | 4.381043893024809e-3 | 1.2135722314051077e-5 | 4.797001418498872e-3 | 7.5529355695499656e-3 | 4.73844793989483e-3 | 6.926038852402484e-2 | 8.690762664778026e-2 | 6.883638529073727e-2 |
| user_003 | Sitting | 1.446047539816e12 | 0.345482 | -0.535886 | 9.38788 | 0.4569588181818181 | -0.6578139090909091 | 9.419232727272725 | 0.119357812324 | 0.287173804996 | 88.13229089439999 | 9.409507028092385 | 9.454137813344296 | 0.1114768181818181 | 0.12192790909090911 | 3.1352727272725645e-2 | 8.075757024793388e-2 | 6.935646280991735e-2 | 5.35213223140498e-2 | 1.242708099194213e-2 | 1.4866415015280998e-2 | 9.829935074379145e-4 | 7.719524672739292e-3 | 8.24246887406912e-3 | 4.164759248685209e-3 | 8.786082558648815e-2 | 9.078804367354283e-2 | 6.453494594934754e-2 |
| user_003 | Sitting | 1.446047539873e12 | 0.460442 | -0.497565 | 9.38788 | 0.4465078181818182 | -0.6856831818181818 | 9.436650909090908 | 0.21200683536400003 | 0.24757092922499999 | 88.13229089439999 | 9.412325358751097 | 9.47302938235518 | 1.3934181818181846e-2 | 0.1881181818181818 | 4.877090909090853e-2 | 7.474023140495867e-2 | 8.48746694214876e-2 | 5.2254545454545755e-2 | 1.9416142294214952e-4 | 3.5388450330578504e-2 | 2.3786015735536644e-3 | 6.82255095679489e-3 | 1.0529558257280991e-2 | 4.3390723510142995e-3 | 8.259873459560316e-2 | 0.1026136358252693 | 6.587163540564557e-2 |
| user_003 | Sitting | 1.446047539995e12 | 0.422122 | -0.689167 | 9.27292 | 0.4325730909090909 | -0.6682648181818182 | 9.440134545454546 | 0.178186982884 | 0.47495115388899994 | 85.98704532639998 | 9.308070877640166 | 9.474249798551595 | 1.045109090909091e-2 | 2.090218181818182e-2 | 0.16721454545454684 | 6.587278512396696e-2 | 5.383837190082646e-2 | 4.908760330578555e-2 | 1.0922530119008267e-4 | 4.3690120476033066e-4 | 2.7960704211570712e-2 | 6.992470474033807e-3 | 4.097512226579267e-3 | 4.275083996994804e-3 | 8.36209930222896e-2 | 6.401181317990662e-2 | 6.538412649102841e-2 |
| user_003 | Sitting | 1.446047540043e12 | 0.537083 | -0.612526 | 9.46452 | 0.44650763636363633 | -0.6264608181818182 | 9.4262 | 0.28845814888899995 | 0.375188100676 | 89.5771388304 | 9.499514991828004 | 9.458218273192353 | 9.057536363636365e-2 | 1.3934818181818187e-2 | 3.8320000000000576e-2 | 5.637189256198345e-2 | 6.998997520661156e-2 | 6.302214876033073e-2 | 8.203896497859506e-3 | 1.9417915776033073e-4 | 1.4684224000000442e-3 | 5.7590313811262195e-3 | 6.7729492727280245e-3 | 5.841695422990275e-3 | 7.588828223860532e-2 | 8.229792994193733e-2 | 7.643098470509375e-2 |
| user_003 | Sitting | 1.446047540172e12 | 0.498763 | -0.765807 | 9.4262 | 0.45347509090909094 | -0.6926505454545454 | 9.45406909090909 | 0.24876453016900002 | 0.586460361249 | 88.85324643999999 | 9.470399745069793 | 9.490850263665708 | 4.528790909090907e-2 | 7.315645454545461e-2 | 2.7869090909090843e-2 | 5.2888297520661126e-2 | 6.555606611570247e-2 | 6.3655537190083e-2 | 2.0509947098264446e-3 | 5.351866841661166e-3 | 7.766862280991699e-4 | 5.354138748736284e-3 | 5.332058540061606e-3 | 5.5967744817431225e-3 | 7.31719806260312e-2 | 7.30209458995267e-2 | 7.481159323088316e-2 |
| user_003 | Sitting | 1.44604754018e12 | 0.652044 | -0.727487 | 9.31124 | 0.4743770909090909 | -0.6926505454545454 | 9.44710181818182 | 0.42516137793599995 | 0.529237335169 | 86.6991903376 | 9.36234954756043 | 9.485110929166572 | 0.17766690909090904 | 3.483645454545459e-2 | 0.13586181818181942 | 6.618956198347105e-2 | 6.080561157024793e-2 | 7.125619834710793e-2 | 3.156553058591734e-2 | 1.2135785652975235e-3 | 1.8458433639669758e-2 | 8.134367837546952e-3 | 4.75284555010293e-3 | 7.026583219834817e-3 | 9.01907303304888e-2 | 6.894088445982492e-2 | 8.382471723683187e-2 |
| user_003 | Sitting | 1.44604754035e12 | 0.345482 | -0.804128 | 9.34956 | 0.44650772727272714 | -0.6647813636363636 | 9.408781818181819 | 0.119357812324 | 0.646621840384 | 87.41427219360001 | 9.390434060591023 | 9.44335301311665 | 0.10102572727272713 | 0.1393466363636363 | 5.9221818181818264e-2 | 5.478842148760328e-2 | 6.143899999999997e-2 | 8.645752066115717e-2 | 1.0206197570983441e-2 | 1.941748506585949e-2 | 3.5072237487603405e-3 | 4.3148482379737e-3 | 5.454524538504126e-3 | 1.1695526568294479e-2 | 6.568750442796331e-2 | 7.38547529851947e-2 | 0.10814585784159501 |
| user_003 | Sitting | 1.446047540358e12 | 0.575403 | -0.727487 | 9.4262 | 0.4569587272727272 | -0.6717486363636365 | 9.4262 | 0.331088612409 | 0.529237335169 | 88.85324643999999 | 9.471724889774723 | 9.461787036578569 | 0.11844427272727281 | 5.573836363636353e-2 | 0.0 | 6.52394214876033e-2 | 6.4922694214876e-2 | 7.188958677685955e-2 | 1.4029045741892582e-2 | 3.1067651808594924e-3 | 0.0 | 5.589113015519157e-3 | 5.709377989187071e-3 | 9.361054894064549e-3 | 7.476037062186862e-2 | 7.55604260786496e-2 | 9.675254463870471e-2 |
| user_003 | Sitting | 1.446047540383e12 | 0.460442 | -0.650847 | 9.38788 | 0.45347490909090915 | -0.6682649999999999 | 9.4262 | 0.21200683536400003 | 0.42360181740899994 | 88.13229089439999 | 9.421671802136444 | 9.461239098852479 | 6.967090909090867e-3 | 1.7417999999999934e-2 | 3.8320000000000576e-2 | 4.623757851239669e-2 | 5.890550413223136e-2 | 6.112198347107458e-2 | 4.854035573553661e-5 | 3.033867239999977e-4 | 1.4684224000000442e-3 | 3.6385488936942142e-3 | 5.2515310322449205e-3 | 7.794443468069084e-3 | 6.032038539079649e-2 | 7.246744808701988e-2 | 8.828614539138678e-2 |
| user_003 | Sitting | 1.446047540423e12 | 0.575403 | -0.650847 | 9.46452 | 0.47437690909090907 | -0.710069 | 9.422716363636363 | 0.331088612409 | 0.42360181740899994 | 89.5771388304 | 9.504305827372034 | 9.461891292898251 | 0.10102609090909093 | 5.9222e-2 | 4.180363636363715e-2 | 6.46059090909091e-2 | 6.55560909090909e-2 | 5.257123966942132e-2 | 1.0206271044371905e-2 | 3.5072452839999997e-3 | 1.7475440132232066e-3 | 5.869341043163786e-3 | 6.055816182386925e-3 | 4.174688476033004e-3 | 7.661162472604133e-2 | 7.781912478553665e-2 | 6.461182922679874e-2 |
| user_003 | Sitting | 1.446047540471e12 | 0.383802 | -0.804128 | 9.4262 | 0.48831172727272726 | -0.7240036363636364 | 9.433167272727273 | 0.147303975204 | 0.646621840384 | 88.85324643999999 | 9.46821906461759 | 9.474308441080531 | 0.10450972727272728 | 8.012436363636355e-2 | 6.967272727273155e-3 | 6.998997520661158e-2 | 8.044088429752065e-2 | 6.428892561983462e-2 | 1.0922283094619836e-2 | 6.419913648132217e-3 | 4.854288925620431e-5 | 6.395622050142e-3 | 8.861414608238164e-3 | 5.910096766942079e-3 | 7.997263313247852e-2 | 9.413508701986824e-2 | 7.687715373855929e-2 |
| user_003 | Sitting | 1.446047540488e12 | 0.383802 | -0.574206 | 9.2346 | 0.4813444545454546 | -0.7205199090909091 | 9.419232727272727 | 0.147303975204 | 0.329712530436 | 85.27783716 | 9.260391658328496 | 9.45974724809149 | 9.754245454545463e-2 | 0.14631390909090913 | 0.18463272727272617 | 6.238931404958679e-2 | 7.664046280991733e-2 | 6.555570247933883e-2 | 9.514530438752082e-3 | 2.140775999346282e-2 | 3.4089243980164885e-2 | 5.660853122831709e-3 | 8.715779145921858e-3 | 7.408306848985668e-3 | 7.523864115487273e-2 | 9.335833731339616e-2 | 8.60715217071574e-2 |
| user_003 | Sitting | 1.446047540504e12 | 0.460442 | -0.689167 | 9.4262 | 0.48134445454545455 | -0.7170362727272727 | 9.422716363636363 | 0.21200683536400003 | 0.47495115388899994 | 88.85324643999999 | 9.462568595748884 | 9.4629710671076 | 2.090245454545453e-2 | 2.7869272727272687e-2 | 3.4836363636365775e-3 | 6.555632231404962e-2 | 7.60070991735537e-2 | 6.61890909090911e-2 | 4.369126060247927e-4 | 7.766963623471052e-4 | 1.2135722314051077e-5 | 6.212486192523672e-3 | 8.411279385727269e-3 | 8.010679974755807e-3 | 7.881932626281242e-2 | 9.171302735013859e-2 | 8.950240206137379e-2 |
| user_003 | Sitting | 1.446047540512e12 | 0.345482 | -0.650847 | 9.69444 | 0.46044254545454544 | -0.7170362727272728 | 9.443618181818183 | 0.119357812324 | 0.42360181740899994 | 93.9821669136 | 9.722403331652776 | 9.482798112951304 | 0.11496054545454543 | 6.618927272727282e-2 | 0.2508218181818176 | 6.682309090909094e-2 | 7.664048760330576e-2 | 8.519074380165295e-2 | 1.3215927011206606e-2 | 4.381019824165301e-3 | 6.291158447603276e-2 | 6.4860912804177336e-3 | 8.490713434833205e-3 | 1.3571047289556675e-2 | 8.053627307255863e-2 | 9.214506733858956e-2 | 0.11649483803824388 |
| user_003 | Sitting | 1.44604754052e12 | 0.422122 | -0.689167 | 9.65612 | 0.45347518181818175 | -0.7065852727272728 | 9.457552727272727 | 0.178186982884 | 0.47495115388899994 | 93.24065345439999 | 9.689880886325332 | 9.495529529037851 | 3.135318181818175e-2 | 1.741827272727281e-2 | 0.19856727272727248 | 6.713976859504135e-2 | 7.062323966942148e-2 | 9.69084297520661e-2 | 9.830220101239627e-4 | 3.033962248016558e-4 | 3.942896179834701e-2 | 6.504845854223894e-3 | 7.882813303996995e-3 | 1.67141993688955e-2 | 8.065262459600366e-2 | 8.878520881316321e-2 | 0.12928340716772396 |
| user_003 | Sitting | 1.44604754069e12 | 0.383802 | -0.612526 | 9.38788 | 0.46392618181818174 | -0.6438792727272727 | 9.461036363636364 | 0.147303975204 | 0.375188100676 | 88.13229089439999 | 9.415666889301043 | 9.495227925366775 | 8.012418181818176e-2 | 3.135327272727273e-2 | 7.315636363636457e-2 | 9.184199999999998e-2 | 6.840652066115696e-2 | 7.759008264462769e-2 | 6.419884512033049e-3 | 9.83027710710744e-4 | 5.351853540496005e-3 | 1.1899797043833205e-2 | 6.3106745274838375e-3 | 7.959930590533333e-3 | 0.10908619089432542 | 7.943975407492043e-2 | 8.921844310753989e-2 |
| user_003 | Sitting | 1.446047540787e12 | 0.422122 | -0.727487 | 9.46452 | 0.4604422727272727 | -0.7100688181818181 | 9.373945454545453 | 0.178186982884 | 0.529237335169 | 89.5771388304 | 9.501818938942849 | 9.412453567045363 | 3.8320272727272675e-2 | 1.7418181818181888e-2 | 9.057454545454746e-2 | 3.4836586776859496e-2 | 6.048880165289261e-2 | 9.152462809917385e-2 | 1.4684433018925579e-3 | 3.033930578512421e-4 | 8.203748284297884e-3 | 1.9130624598204355e-3 | 5.537250364716014e-3 | 1.1115218392186358e-2 | 4.3738569476154975e-2 | 7.441270297950488e-2 | 0.10542873608360463 |
| user_003 | Sitting | 1.446047540891e12 | 0.575403 | -0.574206 | 9.54116 | 0.47437699999999994 | -0.6961342727272727 | 9.429683636363634 | 0.331088612409 | 0.329712530436 | 91.0337341456 | 9.57572635826886 | 9.467791840706656 | 0.10102600000000006 | 0.12192827272727269 | 0.11147636363636515 | 4.718777685950413e-2 | 7.094e-2 | 6.048859504132215e-2 | 1.0206252676000012e-2 | 1.4866503690256189e-2 | 1.2426979649587114e-2 | 3.129960671486852e-3 | 6.173872703349363e-3 | 5.309930136138238e-3 | 5.5946051437852626e-2 | 7.857399508329306e-2 | 7.286926743242475e-2 |
| user_003 | Sitting | 1.446047540981e12 | 0.383802 | -0.535886 | 9.50284 | 0.4604423636363636 | -0.6682649090909091 | 9.457552727272729 | 0.147303975204 | 0.287173804996 | 90.30396806560002 | 9.52567298650337 | 9.493207059307485 | 7.664036363636362e-2 | 0.1323789090909091 | 4.5287272727271954e-2 | 5.162164462809916e-2 | 8.582471900826447e-2 | 4.972099173553766e-2 | 5.873745338314047e-3 | 1.7524175572099177e-2 | 2.05093707107431e-3 | 4.04124672012096e-3 | 1.1492692849347104e-2 | 3.021794856198391e-3 | 6.357080084536422e-2 | 0.1072039777683044 | 5.497085460676767e-2 |
| user_003 | Sitting | 1.446047541102e12 | 0.460442 | -0.727487 | 9.6178 | 0.4395402727272728 | -0.6752321818181817 | 9.45406909090909 | 0.21200683536400003 | 0.529237335169 | 92.50207684000002 | 9.65625812675557 | 9.489015016304148 | 2.090172727272721e-2 | 5.225481818181832e-2 | 0.16373090909091026 | 4.1170479338842964e-2 | 7.79073223140496e-2 | 7.410644628099257e-2 | 4.368822029834685e-4 | 2.7305660232148903e-3 | 2.680781059173592e-2 | 3.656193483053345e-3 | 9.832287709948916e-3 | 9.640176507287873e-3 | 6.046646577280125e-2 | 9.915789282729295e-2 | 9.81844005292484e-2 |
| user_003 | Standing | 1.446047630001e12 | -0.229323 | -9.73276 | 0.382604 | -0.8117916 | -9.410872 | 0.39026859999999997 | 5.2589038329e-2 | 94.72661721760001 | 0.146385820816 | 9.742976551174953 | 9.459500713350298 | 0.5824685999999999 | 0.3218880000000013 | 7.664599999999966e-3 | 0.17269683666666663 | 0.1422949333333335 | 4.560130333333332e-2 | 0.33926966998595987 | 0.10361188454400083 | 5.874609315999948e-5 | 7.34666601397767e-2 | 3.590423294435565e-2 | 3.994914274266721e-3 | 0.27104733929661934 | 0.18948412319863545 | 6.32053342232024e-2 |
| user_003 | Standing | 1.446047631037e12 | -0.804128 | -9.46452 | 0.382604 | -0.7867094545454546 | -9.429683636363636 | 0.38260436363636363 | 0.646621840384 | 89.5771388304 | 0.146385820816 | 9.506321396397242 | 9.47130624798105 | 1.7418545454545353e-2 | 3.4836363636364e-2 | 3.6363636363168084e-7 | 8.32913140495868e-2 | 7.473983471074387e-2 | 7.505725619834712e-2 | 3.034057257520626e-4 | 1.213572231404984e-3 | 1.3223140495527202e-13 | 1.0100381915482346e-2 | 6.751874596543982e-3 | 1.0945504611989483e-2 | 0.10050065629378918 | 8.216979126506274e-2 | 0.10462076568248525 |
| user_003 | Standing | 1.446047631231e12 | -0.727487 | -9.50284 | 0.344284 | -0.8006440909090909 | -9.429683636363636 | 0.396539 | 0.529237335169 | 90.30396806560002 | 0.11853147265599999 | 9.536862003480234 | 9.472512914924552 | 7.315709090909095e-2 | 7.315636363636457e-2 | 5.2254999999999996e-2 | 6.460626446280994e-2 | 6.77725619834712e-2 | 4.845475206611569e-2 | 5.351959950280998e-3 | 5.351853540496005e-3 | 2.7305850249999997e-3 | 5.803188286028555e-3 | 5.375021737640885e-3 | 3.176314455302029e-3 | 7.617866030607623e-2 | 7.331453974240638e-2 | 5.635880104563997e-2 |
| user_003 | Standing | 1.446047631554e12 | -0.804128 | -9.38788 | 0.267643 | -0.783225909090909 | -9.426199999999998 | 0.3826041818181818 | 0.646621840384 | 88.13229089439999 | 7.163277544900001e-2 | 9.426056731753368 | 9.467054139655685 | 2.090209090909101e-2 | 3.83199999999988e-2 | 0.11496118181818177 | 6.0489123966942164e-2 | 7.378975206611571e-2 | 5.985584297520662e-2 | 4.36897404371905e-4 | 1.468422399999908e-3 | 1.3216073325033046e-2 | 5.6498166748760355e-3 | 6.695608974906073e-3 | 4.943748189722764e-3 | 7.516526242138742e-2 | 8.182670086778565e-2 | 7.031179267891528e-2 |
| user_003 | Standing | 1.446047631878e12 | -0.650847 | -9.50284 | 0.420925 | -0.7867095454545456 | -9.41574909090909 | 0.3826042727272727 | 0.42360181740899994 | 90.30396806560002 | 0.177177855625 | 9.534398131955369 | 9.456979008946961 | 0.13586254545454568 | 8.709090909091088e-2 | 3.8320727272727284e-2 | 6.682312396694219e-2 | 6.872264462809935e-2 | 5.573869421487603e-2 | 1.8458631257388492e-2 | 7.584826446281304e-3 | 1.4684781387107448e-3 | 6.99580428544554e-3 | 6.06234491960935e-3 | 4.234333282864011e-3 | 8.364092470462973e-2 | 7.786106163936728e-2 | 6.507175487770413e-2 |
| user_003 | Standing | 1.446047633044e12 | -0.765807 | -9.46452 | 0.420925 | -0.7449053636363636 | -9.412265454545452 | 0.35821872727272724 | 0.586460361249 | 89.5771388304 | 0.177177855625 | 9.504776538523881 | 9.448962586608722 | 2.09016363636364e-2 | 5.225454545454866e-2 | 6.270627272727275e-2 | 4.877114876033059e-2 | 3.008595041322434e-2 | 6.017230578512396e-2 | 4.3687840267768745e-4 | 2.730537520661492e-3 | 3.9320766393471105e-3 | 3.7819710088888084e-3 | 1.1749585694967016e-3 | 4.582958047978211e-3 | 6.149773173775443e-2 | 3.427766867067685e-2 | 6.769754831586008e-2 |
| user_003 | Standing | 1.446047634209e12 | -0.612526 | -9.46452 | 0.420925 | -0.7414217272727274 | -9.422716363636361 | 0.3651860909090909 | 0.375188100676 | 89.5771388304 | 0.177177855625 | 9.493656028459268 | 9.45934015573746 | 0.12889572727272736 | 4.180363636363893e-2 | 5.573890909090912e-2 | 5.162166942148758e-2 | 2.6285619834712035e-2 | 5.5105239669421484e-2 | 1.661410850916531e-2 | 1.747544013223355e-3 | 3.106825986644631e-3 | 4.422985147613826e-3 | 8.947337087904492e-4 | 3.757725303054846e-3 | 6.65055271959694e-2 | 2.9912099705477868e-2 | 6.130028795246272e-2 |
| user_003 | Standing | 1.446047634274e12 | -0.765807 | -9.4262 | 0.382604 | -0.7344543636363636 | -9.426199999999996 | 0.3651860909090909 | 0.586460361249 | 88.85324643999999 | 0.146385820816 | 9.464993006973907 | 9.462215332128153 | 3.135263636363639e-2 | 3.552713678800501e-15 | 1.7417909090909123e-2 | 4.6237793388429725e-2 | 2.406876033058031e-2 | 5.637201652892562e-2 | 9.829878069504147e-4 | 1.2621774483536189e-29 | 3.033835570991747e-4 | 3.7665404000984215e-3 | 8.406745821187829e-4 | 3.784202551679941e-3 | 6.13721467776582e-2 | 2.899438880402177e-2 | 6.151587235567696e-2 |
| user_003 | Standing | 1.446047635698e12 | -0.650847 | -9.46452 | 0.344284 | -0.6612977272727272 | -9.429683636363636 | 0.3442840909090909 | 0.42360181740899994 | 89.5771388304 | 0.11853147265599999 | 9.493117091896897 | 9.459763929176468 | 1.0450727272727223e-2 | 3.4836363636364e-2 | 9.0909090921798e-8 | 5.352161983471075e-2 | 4.7187438016529075e-2 | 7.442391735537189e-2 | 1.0921770052892458e-4 | 1.213572231404984e-3 | 8.264462812227734e-15 | 4.700985087361384e-3 | 3.7013953057851378e-3 | 8.01526277131029e-3 | 6.856373011557484e-2 | 6.08390935647889e-2 | 8.952799992912994e-2 |
| user_003 | Standing | 1.446047636216e12 | -0.727487 | -9.4262 | 0.229323 | -0.6578138181818182 | -9.4262 | 0.35821881818181817 | 0.529237335169 | 88.85324643999999 | 5.2589038329e-2 | 9.45701183321127 | 9.456553236397447 | 6.967318181818183e-2 | 0.0 | 0.12889581818181817 | 5.542190082644626e-2 | 4.9404297520661444e-2 | 6.999004958677685e-2 | 4.854352264669423e-3 | 0.0 | 1.6614131944760326e-2 | 3.833838554132231e-3 | 4.545379630353109e-3 | 8.959636743441019e-3 | 6.191799862828442e-2 | 6.741943065877305e-2 | 9.465535771123058e-2 |
| user_003 | Standing | 1.446047636799e12 | -0.689167 | -9.38788 | 0.267643 | -0.6682649999999999 | -9.433167272727273 | 0.354735 | 0.47495115388899994 | 88.13229089439999 | 7.163277544900001e-2 | 9.416946151685162 | 9.463843905554109 | 2.0902000000000087e-2 | 4.528727272727373e-2 | 8.7092e-2 | 3.261981818181818e-2 | 3.0085950413223535e-2 | 5.7005396694214866e-2 | 4.3689360400000365e-4 | 2.050937071074471e-3 | 7.585016464000001e-3 | 1.9737354860465822e-3 | 1.6206705526671935e-3 | 4.642547116395943e-3 | 4.4426742915124696e-2 | 4.025755274066214e-2 | 6.813623937667783e-2 |
| user_003 | Standing | 1.446047637252e12 | -0.650847 | -9.46452 | 0.305964 | -0.6612977272727273 | -9.412265454545455 | 0.3791206363636364 | 0.42360181740899994 | 89.5771388304 | 9.361396929600001e-2 | 9.4918046027668 | 9.443954330255995 | 1.0450727272727334e-2 | 5.225454545454511e-2 | 7.31566363636364e-2 | 5.320490082644627e-2 | 2.7869090909091006e-2 | 8.424145454545454e-2 | 1.092177005289269e-4 | 2.730537520661121e-3 | 5.351893444041327e-3 | 4.737409643812922e-3 | 1.1694423320811303e-3 | 9.865432293460557e-3 | 6.882884310965079e-2 | 3.419710999603812e-2 | 9.932488254944255e-2 |
| user_003 | Standing | 1.446047637576e12 | -0.689167 | -9.38788 | 0.420925 | -0.6543302727272727 | -9.415749090909088 | 0.3547350909090909 | 0.47495115388899994 | 88.13229089439999 | 0.177177855625 | 9.422548482439026 | 9.446163013538891 | 3.483672727272724e-2 | 2.7869090909089067e-2 | 6.61899090909091e-2 | 6.270593388429752e-2 | 2.881917355371997e-2 | 9.342565289256198e-2 | 1.213597567074378e-3 | 7.766862280990708e-4 | 4.381104065462812e-3 | 5.450126404488353e-3 | 1.1087637205109233e-3 | 1.221649972368294e-2 | 7.382497141542524e-2 | 3.32981038575911e-2 | 0.1105282756749735 |
| user_003 | Standing | 1.446047638094e12 | -0.650847 | -9.38788 | 0.420925 | -0.6194936363636364 | -9.426199999999998 | 0.35125154545454546 | 0.42360181740899994 | 88.13229089439999 | 0.177177855625 | 9.41982327686852 | 9.453465812133908 | 3.135336363636354e-2 | 3.83199999999988e-2 | 6.967345454545454e-2 | 5.5738801652892515e-2 | 4.908760330578507e-2 | 4.750456198347106e-2 | 9.830334113140435e-4 | 1.468422399999908e-3 | 4.85439026829752e-3 | 4.977944543102175e-3 | 3.370421060856468e-3 | 2.643432850076633e-3 | 7.055455012330654e-2 | 5.8055327583749525e-2 | 5.141432533911763e-2 |
| user_003 | Standing | 1.446047638482e12 | -0.612526 | -9.4262 | 0.305964 | -0.6299447272727271 | -9.426199999999996 | 0.33731672727272727 | 0.375188100676 | 88.85324643999999 | 9.361396929600001e-2 | 9.451034256099804 | 9.453548497680739 | 1.7418727272727086e-2 | 3.552713678800501e-15 | 3.1352727272727254e-2 | 3.325333057851239e-2 | 3.5469752066116585e-2 | 5.0671570247933874e-2 | 3.0341205980164636e-4 | 1.2621774483536189e-29 | 9.829935074380154e-4 | 1.6030517021359868e-3 | 1.855662266566537e-3 | 3.1818393299639365e-3 | 4.0038128104795144e-2 | 4.307739855848467e-2 | 5.640779493974159e-2 |
| user_003 | Standing | 1.446047639065e12 | -0.612526 | -9.46452 | 0.191003 | -0.5811732727272727 | -9.429683636363636 | 0.3198984545454545 | 0.375188100676 | 89.5771388304 | 3.6482146009e-2 | 9.486243148743606 | 9.453426664457025 | 3.135272727272731e-2 | 3.4836363636364e-2 | 0.1288954545454545 | 4.3387553719008265e-2 | 2.5335537190083555e-2 | 5.9222231404958664e-2 | 9.829935074380188e-4 | 1.213572231404984e-3 | 1.661403820247933e-2 | 2.7835533993929387e-3 | 9.179019059354219e-4 | 5.252647062770848e-3 | 5.2759391575272534e-2 | 3.0296895978555657e-2 | 7.24751478975438e-2 |
| user_003 | Standing | 1.446047640683e12 | -0.650847 | -9.50284 | 0.382604 | -0.647363 | -9.426200000000001 | 0.28506163636363635 | 0.42360181740899994 | 90.30396806560002 | 0.146385820816 | 9.53278320868701 | 9.453927396237697 | 3.4839999999999316e-3 | 7.663999999999938e-2 | 9.754236363636365e-2 | 7.252361983471071e-2 | 6.36555371900822e-2 | 0.10514338016528924 | 1.2138255999999524e-5 | 5.8736895999999044e-3 | 9.514512703768598e-3 | 8.447700141810665e-3 | 5.77770706897057e-3 | 1.3642986799874529e-2 | 9.19113711235485e-2 | 7.601122988723817e-2 | 0.1168031968735211 |
| user_003 | Standing | 1.446047640747e12 | -0.689167 | -9.4262 | 0.229323 | -0.657814 | -9.422716363636361 | 0.28506163636363635 | 0.47495115388899994 | 88.85324643999999 | 5.2589038329e-2 | 9.454141242451268 | 9.45114977637439 | 3.1352999999999964e-2 | 3.483636363638354e-3 | 5.573863636363635e-2 | 7.030674380165286e-2 | 6.048859504132199e-2 | 0.110210520661157 | 9.830106089999977e-4 | 1.2135722314063454e-5 | 3.1067955836776846e-3 | 8.254628780476328e-3 | 5.645317370999117e-3 | 1.3925422762026294e-2 | 9.085498764776939e-2 | 7.513532705058998e-2 | 0.11800602849865889 |
| user_003 | Standing | 1.446047641654e12 | -0.842448 | -9.57948 | 0.267643 | -0.6508466363636364 | -9.44361818181818 | 0.2571923636363636 | 0.7097186327039999 | 91.7664370704 | 7.163277544900001e-2 | 9.620176114736829 | 9.470404615826363 | 0.1916013636363636 | 0.13586181818181942 | 1.0450636363636412e-2 | 8.170772727272727e-2 | 8.297388429752059e-2 | 4.592101652892564e-2 | 3.671108254731403e-2 | 1.8458433639669758e-2 | 1.0921580040495968e-4 | 1.1734313433839967e-2 | 1.353243362764841e-2 | 3.3395934129819694e-3 | 0.10832503604356643 | 0.11632898876741089 | 5.7789215369149716e-2 |
| user_003 | Standing | 1.446047643467e12 | -0.574206 | -9.38788 | 0.305964 | -0.5707224545454546 | -9.429683636363636 | 0.23977409090909088 | 0.329712530436 | 88.13229089439999 | 9.361396929600001e-2 | 9.410399427980302 | 9.450245402255211 | 3.4835454545454336e-3 | 4.180363636363715e-2 | 6.618990909090913e-2 | 3.0402719008264464e-2 | 3.420297520661173e-2 | 4.9087950413223155e-2 | 1.2135088933884152e-5 | 1.7475440132232066e-3 | 4.381104065462815e-3 | 1.3481823589834728e-3 | 1.5379269914350359e-3 | 3.5050756410142745e-3 | 3.67176028490896e-2 | 3.921641227133145e-2 | 5.920367928612439e-2 |
| user_003 | Standing | 1.446047643855e12 | -0.497565 | -9.34956 | 0.191003 | -0.5324019999999998 | -9.41574909090909 | 0.22235581818181815 | 0.24757092922499999 | 87.41427219360001 | 3.6482146009e-2 | 9.364738398312792 | 9.434503415765334 | 3.483699999999984e-2 | 6.618909090908964e-2 | 3.135281818181815e-2 | 7.632391735537186e-2 | 5.162115702479397e-2 | 5.732197520661156e-2 | 1.2136165689999889e-3 | 4.380995755371733e-3 | 9.829992079421466e-4 | 1.4299450434075128e-2 | 4.027956560781424e-3 | 5.122443936664913e-3 | 0.11958030955836804 | 6.346618438807727e-2 | 7.157125076918044e-2 |
| user_003 | Standing | 1.446047644438e12 | -0.574206 | -9.34956 | 0.267643 | -0.5115 | -9.4262 | 0.26067590909090915 | 0.329712530436 | 87.41427219360001 | 7.163277544900001e-2 | 9.37099874610412 | 9.444219230410008 | 6.270600000000004e-2 | 7.663999999999938e-2 | 6.967090909090867e-3 | 6.33393388429752e-2 | 6.112198347107474e-2 | 5.732199173553718e-2 | 3.932042436000005e-3 | 5.8736895999999044e-3 | 4.854035573553661e-5 | 5.187536171562735e-3 | 5.468797773704045e-3 | 5.711591336277234e-3 | 7.202455256065625e-2 | 7.395132029723367e-2 | 7.557507086518168e-2 |
| user_003 | Standing | 1.446047645539e12 | -0.612526 | -9.38788 | 0.344284 | -0.5045326363636363 | -9.4262 | 0.2780943636363636 | 0.375188100676 | 88.13229089439999 | 0.11853147265599999 | 9.414138859594752 | 9.444441031280194 | 0.10799336363636369 | 3.8320000000000576e-2 | 6.61896363636364e-2 | 3.9904024793388414e-2 | 2.8819173553720292e-2 | 7.442387603305785e-2 | 1.166256658949588e-2 | 1.4684224000000442e-3 | 4.3810679619504175e-3 | 2.9523507825371885e-3 | 1.2830768228400398e-3 | 8.304296049558978e-3 | 5.43355388538403e-2 | 3.582006173696578e-2 | 9.112791037634396e-2 |
| user_003 | Standing | 1.44604764651e12 | -0.574206 | -9.54116 | 0.344284 | -0.4940816363636364 | -9.426199999999998 | 0.2885453636363636 | 0.329712530436 | 91.0337341456 | 0.11853147265599999 | 9.56462117120652 | 9.444022785372637 | 8.01243636363636e-2 | 0.11496000000000173 | 5.573863636363635e-2 | 6.523946280991734e-2 | 4.9720991735537984e-2 | 5.352174380165291e-2 | 6.419913648132226e-3 | 1.3215801600000398e-2 | 3.1067955836776846e-3 | 5.659752639225393e-3 | 3.632993961833309e-3 | 3.566852951106687e-3 | 7.52313275120504e-2 | 6.027432257465287e-2 | 5.9723135811063095e-2 |
| user_003 | Walking | 1.446047680232e12 | -2.60518 | -13.21991 | -2.37646 | -3.0824427272727264 | -9.199761818181818 | -1.8643604545454544 | 6.7869628323999995 | 174.76602040810002 | 5.647562131599999 | 13.68212503129905 | 10.110920866037462 | 0.47726272727272656 | 4.020148181818183 | 0.5120995454545454 | 1.095463367703004 | 1.074938472222222 | 1.4198113878328742 | 0.22777971084380097 | 16.16159140377604 | 0.262245944454752 | 3.853906554939335 | 2.2260635748609405 | 2.7087737554357663 | 1.9631369170130073 | 1.491999857527118 | 1.645835275911829 |
| user_003 | Walking | 1.44604768049e12 | -1.83878 | -9.11964 | 1.30229 | -2.922195454545455 | -8.931519999999999 | -1.1536927272727266 | 3.3811118884000004 | 83.1678337296 | 1.6959592440999998 | 9.393875923286405 | 9.851355369488441 | 1.0834154545454548 | 0.1881200000000014 | 2.4559827272727266 | 1.3440594227994227 | 1.1076583262167126 | 1.870075981634527 | 1.1737890471479346 | 3.538913440000053e-2 | 6.03185115666198 | 4.202021573533049 | 2.296666948917856 | 5.085103043085589 | 2.0498833072965517 | 1.5154758160122042 | 2.2550173043871724 |
| user_003 | Walking | 1.446047681137e12 | -1.45557 | -10.92069 | -0.230521 | -2.2672662727272734 | -9.366975454545456 | 0.8598665454545457 | 2.1186840249000003 | 119.26147007610001 | 5.3139931441e-2 | 11.019677582962263 | 9.76233662350399 | 0.8116962727272734 | 1.553714545454545 | 1.0903875454545457 | 1.1496087768595042 | 0.7835095867768593 | 1.4846749090909093 | 0.6588508391593482 | 2.414028888757023 | 1.188944999282389 | 1.9014183976505534 | 0.8769101837920356 | 2.724071209736771 | 1.3789192861261146 | 0.936434826238343 | 1.6504760554872557 |
| user_003 | Walking | 1.446047681525e12 | -6.93538 | -8.69811 | -5.25048 | -3.0650254545454545 | -9.098733636363635 | -0.8157763636363636 | 48.0994957444 | 75.65711757209999 | 27.567540230399995 | 12.301388277218958 | 9.864638111047226 | 3.870354545454546 | 0.40062363636363507 | 4.434703636363636 | 1.1730446528925622 | 1.096404545454545 | 1.4181680495867768 | 14.979644307520664 | 0.1604992980132221 | 19.66659634237686 | 2.67966856308145 | 1.5218183581554465 | 3.288253171321321 | 1.6369693225841009 | 1.2336200217876843 | 1.813354121875074 |
| user_003 | Walking | 1.446047682692e12 | -2.10702 | -8.27659 | 0.152682 | -2.5738290000000004 | -9.213695454545455 | 0.553303 | 4.439533280399999 | 68.5019420281 | 2.3311793124000002e-2 | 8.541942817744918 | 9.650275824738793 | 0.4668090000000005 | 0.9371054545454545 | 0.400621 | 0.9624420661157022 | 0.7081338842975208 | 0.8851666115702478 | 0.21791064248100045 | 0.8781666329388428 | 0.16049718564100002 | 1.4990533388927312 | 0.7491488318305034 | 1.0793257083214003 | 1.224358337617191 | 0.8655338421058436 | 1.0389060151531515 |
| user_003 | Walking | 1.446047683792e12 | -1.53221 | -10.30757 | 0.191003 | -2.1836581818181813 | -9.527224545454546 | 0.7762576363636363 | 2.3476674841 | 106.2459993049 | 3.6482146009e-2 | 10.422578804451852 | 9.861013228604087 | 0.6514481818181812 | 0.7803454545454542 | 0.5852546363636363 | 1.011212925619835 | 0.5805068595041317 | 1.0647335785123966 | 0.4243847335942141 | 0.6089390284297516 | 0.34252298938513215 | 1.5091309276978426 | 0.5027277192944397 | 1.3829956132456893 | 1.2284669013440463 | 0.7090329465507507 | 1.1760083389354385 |
| user_003 | Walking | 1.446047684504e12 | -2.29862 | -9.38788 | 1.14901 | -2.984901181818182 | -9.025578181818181 | -0.7495872727272729 | 5.2836539044 | 88.13229089439999 | 1.3202239801000002 | 9.733250678930446 | 9.901130370996102 | 0.686281181818182 | 0.36230181818181784 | 1.8985972727272729 | 1.408033049586777 | 1.0112117355371895 | 1.7253640330578512 | 0.4709818605177606 | 0.13126260745785098 | 3.6046716040074385 | 3.9676291810006052 | 1.263078076126069 | 4.308234068216036 | 1.9918908556948107 | 1.1238674637723387 | 2.075628595923663 |
| user_003 | Walking | 1.446047684828e12 | -1.76214 | -9.15796 | 0.957409 | -2.664405727272727 | -9.227629090909092 | 6.907372727272726e-2 | 3.1051373796000004 | 83.86823136159998 | 0.9166319932809999 | 9.374966705779865 | 9.994297150018252 | 0.9022657272727268 | 6.96690909090929e-2 | 0.8883352727272726 | 1.6743753223140496 | 0.6954660330578512 | 1.6946447685950412 | 0.8140834426109826 | 4.853782228099451e-3 | 0.7891395567714379 | 4.344463523200128 | 0.7648548299700221 | 4.110607909633657 | 2.0843376701485123 | 0.874559792106876 | 2.027463417582092 |
| user_003 | Walking | 1.446047686769e12 | -3.06503 | -7.89339 | -1.34181 | -2.5598954545454546 | -9.286853636363636 | -0.19916863636363635 | 9.3944089009 | 62.3056056921 | 1.8004540760999999 | 8.573241433034532 | 9.80327103142926 | 0.5051345454545455 | 1.3934636363636361 | 1.1426413636363635 | 0.9272887768595044 | 0.6948344628099171 | 1.5445286115702481 | 0.2551609090115703 | 1.941740905867768 | 1.3056292858927683 | 1.2242473702419867 | 0.7480118161219383 | 2.5759417080364972 | 1.1064571253518984 | 0.8648767635460779 | 1.6049740521380702 |
| user_003 | Walking | 1.44604768774e12 | -3.63983 | -11.57214 | 0.459245 | -2.1801745454545456 | -9.283367272727272 | 1.239585 | 13.248362428899998 | 133.9144241796 | 0.210905970025 | 12.13975669354724 | 9.650840173435693 | 1.4596554545454543 | 2.288772727272727 | 0.7803399999999999 | 1.012479917355372 | 0.5751234710743808 | 1.634154388429752 | 2.1305940459842967 | 5.238480597107437 | 0.6089305155999999 | 1.130916245436589 | 0.7299280737819693 | 3.609722345973299 | 1.0634454595495666 | 0.8543582818595307 | 1.899926931745876 |
| user_003 | Walking | 1.446047688711e12 | -1.11069 | -8.85139 | 1.30229 | -2.389194454545455 | -9.178858181818182 | -0.22703809090909088 | 1.2336322760999998 | 78.34710493210001 | 1.6959592440999998 | 9.015358919771304 | 9.831458270536645 | 1.278504454545455 | 0.3274681818181815 | 1.529328090909091 | 1.5356619504132227 | 0.6565134710743803 | 1.5765156528925617 | 1.6345736402925712 | 0.10723541010330558 | 2.3388444096436447 | 4.659950346595945 | 0.821465350060331 | 3.048926094202544 | 2.1586918137140247 | 0.9063472568835473 | 1.7461174342530756 |
| user_003 | Walking | 1.446047688969e12 | -2.56686 | -9.04299 | 2.10702 | -2.0303762727272727 | -9.241563636363635 | 0.8389629090909092 | 6.5887702596 | 81.7756681401 | 4.439533280399999 | 9.633481804628065 | 9.774966473369737 | 0.5364837272727274 | 0.19857363636363523 | 1.2680570909090907 | 1.5451640826446285 | 0.5602380991735537 | 1.4526878760330577 | 0.28781478962843815 | 3.943148905867724e-2 | 1.607968785804826 | 4.602630817579073 | 0.6797381719076636 | 2.5772022493239755 | 2.145374283797369 | 0.8244623532385598 | 1.6053667024465081 |
| user_003 | Walking | 1.446047690393e12 | -2.68182 | -10.11596 | 0.344284 | -1.7063949090909092 | -9.42968181818182 | 0.32338163636363637 | 7.192158512400001 | 102.33264672159999 | 0.11853147265599999 | 10.471071421141964 | 9.760359424951764 | 0.9754250909090909 | 0.6862781818181798 | 2.0902363636363608e-2 | 0.9624424297520658 | 0.6628478512396693 | 1.677226404958678 | 0.9514541079750083 | 0.47097774283966665 | 4.369088055867757e-4 | 1.3556672768307587 | 0.9661630698631855 | 4.791137845034764 | 1.164331257345073 | 0.9829359439267573 | 2.1888667947215894 |
| user_003 | Walking | 1.446047690652e12 | -2.18366 | -9.92436 | 1.03405 | -2.2881672727272724 | -9.408779090909091 | 0.4905977272727272 | 4.768370995600001 | 98.4929214096 | 1.0692594024999997 | 10.214232805634499 | 9.71814603286323 | 0.10450727272727223 | 0.5155809090909091 | 0.5434522727272727 | 0.5843059173553716 | 0.6349776859504132 | 0.7952254297520661 | 1.0921770052892457e-2 | 0.26582367381900834 | 0.29534037273243796 | 0.5730626514054392 | 0.8052313051819684 | 0.9567104758614733 | 0.7570090167266432 | 0.8973468143265281 | 0.978115778352171 |
| user_003 | Walking | 1.446047690976e12 | -3.48655 | -8.50651 | -1.53341 | -2.6818227272727273 | -9.426197272727274 | 0.19100236363636364 | 12.1560309025 | 72.36071238010001 | 2.3513462280999997 | 9.320305226262711 | 9.863548615477232 | 0.8047272727272725 | 0.9196872727272734 | 1.7244123636363635 | 0.6884989586776858 | 0.7135176859504131 | 0.6441610743801652 | 0.647585983471074 | 0.8458246796165301 | 2.97359799986195 | 0.6691618438304583 | 0.8418925829534188 | 0.7191321415054394 | 0.8180231315986476 | 0.917547046724809 | 0.8480165927064396 |
| user_003 | Walking | 1.446047691623e12 | -1.64717 | -8.58315 | 1.41725 | -2.9535481818181823 | -8.959386363636362 | 0.7797413636363636 | 2.7131690089 | 73.67046392249999 | 2.0085975625 | 8.853938699465905 | 9.648125389375675 | 1.3063781818181823 | 0.37623636363636237 | 0.6375086363636363 | 1.0663186776859506 | 0.533318842975206 | 1.0739179256198348 | 1.7066239539305799 | 0.1415538013223131 | 0.40641726143822304 | 2.1636084182915107 | 0.3901598033894812 | 1.435733955825954 | 1.4709209422302447 | 0.6246277318447214 | 1.1982211631522597 |
| user_003 | Walking | 1.446047692077e12 | -2.41358 | -8.46819 | -2.10822 | -2.709691818181818 | -9.38787727272727 | 2.030081818181814e-2 | 5.8253684164 | 71.7102418761 | 4.444591568400001 | 9.054291902788423 | 9.958116842269988 | 0.2961118181818181 | 0.9196872727272698 | 2.1285208181818183 | 0.9374227272727272 | 0.6523962809917353 | 1.5017745041322315 | 8.76822088669421e-2 | 0.8458246796165235 | 4.530600873433397 | 1.0978708347835462 | 0.7347211416681446 | 3.0796362454221735 | 1.0477933168251963 | 0.8571587610636343 | 1.7548892402149412 |
| user_003 | Walking | 1.446047692206e12 | -3.44823 | -7.39522 | -0.958607 | -2.765430909090909 | -9.17885818181818 | -0.4256080000000001 | 11.8902901329 | 54.6892788484 | 0.918927380449 | 8.21574685355805 | 9.781032965283147 | 0.6827990909090911 | 1.78363818181818 | 0.5329989999999999 | 0.9529404958677685 | 0.9013192561983469 | 1.4897399338842974 | 0.4662145985462812 | 3.1813651636396627 | 0.2840879340009999 | 1.114687503154771 | 1.157924755509316 | 3.0758563183567618 | 1.0557876221829705 | 1.0760691220871064 | 1.7538119392787705 |
| user_003 | Walking | 1.446047692335e12 | -2.95007 | -8.23827 | -3.75599 | -2.9221963636363637 | -9.09525090909091 | -1.167628 | 8.702913004900001 | 67.8690925929 | 14.107460880100001 | 9.52257667219855 | 9.797101595441392 | 2.787363636363649e-2 | 0.8569809090909093 | 2.588362 | 0.7831913223140495 | 1.0074122314049585 | 1.6192693057851244 | 7.769396041322384e-4 | 0.7344162785462813 | 6.699617843044 | 0.9068448349658151 | 1.327664420832306 | 3.5648362590784433 | 0.9522840096136316 | 1.1522432125347088 | 1.8880773975339156 |
| user_003 | Walking | 1.446047693694e12 | -9.19628 | -8.73643 | -3.64103 | -3.5004845454545457 | -9.070865454545455 | -0.7774568181818182 | 84.57156583839999 | 76.3252091449 | 13.257099460900001 | 13.196737265104584 | 10.011755975240877 | 5.695795454545454 | 0.33443545454545465 | 2.863573181818182 | 1.3054245454545457 | 1.154677272727273 | 1.410566652892562 | 32.442085860020654 | 0.11184707325702486 | 8.200051367628307 | 3.962112980114124 | 1.8664618187049589 | 2.4746358085191984 | 1.9905057096411767 | 1.366185133393333 | 1.5730975203461477 |
| user_003 | Walking | 1.44604769376e12 | -6.59049 | -10.99733 | -2.18486 | -3.7338900000000006 | -9.234597272727273 | -1.1188559090909092 | 43.4345584401 | 120.9412671289 | 4.7736132196000005 | 13.005746375683328 | 10.27047226761441 | 2.8565999999999994 | 1.7627327272727271 | 1.0660040909090909 | 1.4824579338842974 | 1.3066912396694215 | 1.4042332314049588 | 8.160163559999996 | 3.1072266677983467 | 1.1363647218349173 | 4.6287911080828685 | 2.1481911001675433 | 2.460691667583589 | 2.151462550936657 | 1.4656708703414771 | 1.5686591942112822 |
| user_003 | Walking | 1.446047694407e12 | -4.17632 | -9.54116 | 1.49389 | -2.7062089090909094 | -9.182341818181818 | 0.8110947272727274 | 17.441648742399998 | 91.0337341456 | 2.2317073320999996 | 10.52174368724595 | 9.748098079740982 | 1.4701110909090902 | 0.35881818181818126 | 0.6827952727272726 | 1.3345601818181814 | 0.5314178512396698 | 1.4830916033057853 | 2.1612266196139154 | 0.12875048760330537 | 0.4662093844587106 | 2.2976739074978894 | 0.4642471196049591 | 2.6074609343048256 | 1.515808004827092 | 0.681356822527638 | 1.614763429826433 |
| user_003 | Walking | 1.446047694535e12 | -1.60885 | -9.88604 | -1.11189 | -2.4832543636363638 | -9.154472727272728 | 0.9992127272727273 | 2.5883983225 | 97.7337868816 | 1.2362993721000002 | 10.077622962593907 | 9.618689978504337 | 0.8744043636363639 | 0.731567272727272 | 2.111102727272727 | 1.1771632396694214 | 0.5098827272727274 | 1.5508645123966942 | 0.7645829911463145 | 0.5351906745256187 | 4.456754725098347 | 1.7062150581021485 | 0.38990555090390694 | 2.872690375200854 | 1.3062216726506066 | 0.6244241754640085 | 1.6949012877453524 |
| user_003 | Walking | 1.446047694665e12 | -2.87342 | -9.31124 | -1.26517 | -2.444933818181818 | -9.474969999999999 | 0.41395727272727273 | 8.2565424964 | 86.6991903376 | 1.6006551288999997 | 9.826311004792185 | 9.933644266781107 | 0.42848618181818177 | 0.16372999999999927 | 1.6791272727272726 | 1.1565773719008263 | 0.6859657024793389 | 1.47200647107438 | 0.18360040800912392 | 2.680751289999976e-2 | 2.8194683980165283 | 1.6581222852926367 | 1.0010658402313288 | 2.4672273237565943 | 1.287680971860902 | 1.000532778189365 | 1.5707410110379731 |
| user_003 | Walking | 1.446047695054e12 | -5.78577 | -11.34221 | -2.79798 | -3.3855238181818184 | -9.231112727272729 | -1.188528090909091 | 33.475134492900004 | 128.6457276841 | 7.8286920804 | 13.036470160952312 | 10.174146227634331 | 2.400246181818182 | 2.111097272727271 | 1.609451909090909 | 1.5571979338842974 | 1.3351935537190078 | 1.4561710082644626 | 5.761181733332761 | 4.456731694916521 | 2.5903354476763716 | 4.214953860726055 | 2.375827022608414 | 2.522741711639002 | 2.053035279951627 | 1.5413717989532616 | 1.588314109878459 |
| user_003 | Walking | 1.446047695377e12 | -3.21831 | -8.77475 | 1.18733 | -3.3263024545454543 | -8.729464545454546 | -0.6555277272727272 | 10.357519256099998 | 76.99623756249999 | 1.4097525289 | 9.421438815143894 | 9.625389020221588 | 0.10799245454545447 | 4.528545454545352e-2 | 1.8428577272727273 | 1.2569685041322314 | 1.0565014049586776 | 1.5752488925619834 | 1.1662370238752051e-2 | 2.050772393388337e-3 | 3.396124602968802 | 3.6424844351548686 | 1.6223155247297527 | 2.8198124686093284 | 1.908529390697159 | 1.2737015053495668 | 1.679229724787329 |
| user_004 | Jogging | 1.446046317704e12 | 14.94552 | -16.51545 | -2.33814 | 7.7604615 | -9.75192 | -2.5488999999999997 | 223.36856807040002 | 272.76008870250007 | 5.466898659600001 | 22.396329061533724 | 13.252263282051047 | 7.1850585 | 6.763530000000001 | 0.21075999999999961 | 3.59252925 | 3.3817650000000006 | 0.10537999999999981 | 51.62506564842225 | 45.74533806090002 | 4.4419777599999835e-2 | 25.812532824211125 | 22.87266903045001 | 2.2209888799999918e-2 | 5.0806035885720435 | 4.782537927758651 | 0.14902982520287647 |
| user_004 | Jogging | 1.446046317963e12 | -4.21464 | -2.60518 | 5.59417 | 4.809798833333334 | -6.028463333333332 | -1.2140754999999996 | 17.7631903296 | 6.7869628323999995 | 31.2947379889 | 7.472943941372771 | 12.720995087782624 | 9.024438833333335 | 3.423283333333332 | 6.8082455 | 7.036142697222222 | 5.500772777777777 | 1.9598709055555552 | 81.44049625657472 | 11.71886878027777 | 46.35220678827025 | 68.62380124906085 | 44.288261653064815 | 9.080189820087265 | 8.28394840936741 | 6.654942648367814 | 3.0133353315034928 |
| user_004 | Jogging | 1.446046318157e12 | 11.99486 | -7.93171 | 10.53749 | 6.123333666666667 | -6.1561988888888886 | 0.1356518888888893 | 143.87666641959999 | 62.9120235241 | 111.0386955001 | 17.827713971336873 | 12.839999546747189 | 5.871526333333332 | 1.7755111111111113 | 10.401838111111111 | 5.732818960185185 | 3.934486102292769 | 3.6105050307319217 | 34.47482148302676 | 3.1524397056790128 | 108.19823608976357 | 50.7951990084904 | 29.90219635088306 | 24.076787892817315 | 7.1270750668482785 | 5.468290075597952 | 4.906810358350659 |
| user_004 | Jogging | 1.446046318221e12 | 18.24107 | -10.26924 | -5.74865 | 7.3351073000000016 | -6.567502999999999 | -0.4527782999999996 | 332.7366347449 | 105.4572901776 | 33.04697682249999 | 21.708083787957886 | 13.72680797086826 | 10.9059627 | 3.7017370000000005 | 5.2958717 | 6.250133334166667 | 3.9112111920634924 | 3.7790416976587293 | 118.94002241379128 | 13.702856817169003 | 28.046257062860892 | 57.60968134902049 | 28.28226239751165 | 24.47373480982167 | 7.590104172474874 | 5.318107031408042 | 4.947093571969472 |
| user_004 | Jogging | 1.44604631971e12 | 10.99853 | -9.15796 | -4.06255 | 7.389451636363637 | -5.325925181818182 | -0.8366772727272725 | 120.96766216090002 | 83.86823136159998 | 16.5043125025 | 14.877506713996132 | 12.867072032207199 | 3.609078363636364 | 3.8320348181818176 | 3.2258727272727272 | 6.017872429752068 | 5.935532809917354 | 3.272427537190083 | 13.025446634868134 | 14.684490847757756 | 10.406254852561982 | 58.06359185692188 | 51.03102945179534 | 14.182854723662182 | 7.619946972054457 | 7.143600594363836 | 3.766013107207964 |
| user_004 | Jogging | 1.446046321588e12 | 0.268841 | -1.22565 | -2.8363 | 7.4417064545454545 | -5.629003000000001 | -1.2163972727272727 | 7.2275483281e-2 | 1.5022179224999999 | 8.04459769 | 3.101465959152381 | 12.740650178610219 | 7.172865454545454 | 4.403353000000001 | 1.6199027272727273 | 6.5717763636363635 | 4.9743563966942155 | 3.4694130826446288 | 51.44999882901157 | 19.389517642609007 | 2.62408484582562 | 58.40254466676288 | 42.89118429085536 | 16.619887602393 | 7.642155760435852 | 6.549136148443957 | 4.076749636952581 |
| user_004 | Jogging | 1.446046321782e12 | 17.43634 | -13.52647 | 1.99206 | 6.964443727272727 | -4.921818181818182 | -1.3278746363636367 | 304.0259525956 | 182.9653906609 | 3.9683030435999997 | 22.15760921895907 | 12.77352648404185 | 10.471896272727275 | 8.604651818181818 | 3.3199346363636364 | 7.435724636363637 | 5.124153950413224 | 2.9639652231404954 | 109.66061154675938 | 74.04003291213967 | 11.02196598972695 | 83.41708559523506 | 48.905702732130756 | 12.350139861925124 | 9.133295440049833 | 6.993261237229077 | 3.514276577323578 |
| user_004 | Jogging | 1.446046322106e12 | 2.8363 | 3.2195 | -0.728685 | 6.306031000000001 | -4.747634545454545 | -0.878481909090909 | 8.04459769 | 10.36518025 | 0.530981829225 | 4.352098317963991 | 12.54258969286149 | 3.469731000000001 | 7.967134545454545 | 0.14979690909090893 | 8.468790429752065 | 5.474736917355371 | 2.3350062644628102 | 12.039033212361005 | 63.475232865375204 | 2.2439113973190032e-2 | 101.23215893633261 | 49.921877744916806 | 8.150795957230889 | 10.061419330111066 | 7.06554157477803 | 2.8549598871491852 |
| user_004 | Jogging | 1.44604632366e12 | 5.32712 | -4.5212 | -1.45677 | 6.288613636363636 | -3.427327727272727 | -0.42908963636363634 | 28.378207494399998 | 20.441249440000004 | 2.1221788328999995 | 7.137340945148971 | 11.923127322944014 | 0.9614936363636364 | 1.0938722727272734 | 1.0276803636363636 | 7.177616363636364 | 4.30961020661157 | 2.8708571983471076 | 0.9244700127677686 | 1.1965565490415304 | 1.0561269298037685 | 77.96189106622585 | 30.35993183984033 | 14.367687461632316 | 8.829603109213112 | 5.50998474043625 | 3.7904732503517704 |
| user_004 | Jogging | 1.446046324243e12 | 18.31771 | -4.78944 | -4.75232 | 7.950321818181819 | -4.747635454545454 | -0.6137233636363636 | 335.53849964410006 | 22.938735513599998 | 22.5845453824 | 19.52080378826907 | 13.319626558125197 | 10.367388181818182 | 4.180454545454548e-2 | 4.138596636363636 | 7.783457933884298 | 4.992408388429752 | 2.9075944876033057 | 107.48273771250331 | 1.7476200206611595e-3 | 17.127982118520404 | 88.54589721943135 | 43.57792982133113 | 14.303951790608876 | 9.409882954608488 | 6.60135818005137 | 3.782056555712629 |
| user_004 | Jogging | 1.44604632502e12 | 10.76861 | -6.89706 | -2.8363 | 7.431255909090908 | -5.155225454545455 | -0.7461031818181819 | 115.96296133210001 | 47.5694366436 | 8.04459769 | 13.098740232010863 | 12.689672224531755 | 3.337354090909093 | 1.7418345454545445 | 2.090196818181818 | 7.685599090909091 | 5.095651487603306 | 2.1465720082644633 | 11.13793232810766 | 3.0339875837388397 | 4.368922738737396 | 83.11050275563464 | 42.10401614099903 | 7.893255732608974 | 9.11649618853837 | 6.488760755413859 | 2.8094938570157035 |
| user_004 | Jogging | 1.446046325862e12 | 2.4531 | 1.53341 | -2.6447 | 7.424289090909091 | -5.782283636363636 | -0.9133188181818181 | 6.01769961 | 2.3513462280999997 | 6.994438089999999 | 3.919628034405816 | 12.74499084081831 | 4.971189090909091 | 7.315693636363636 | 1.7313811818181817 | 7.254255867768593 | 4.8524280165289255 | 2.1795067768595042 | 24.712720977573554 | 53.5193733811314 | 2.9976807967541235 | 85.1353323510852 | 38.528028298417354 | 6.254722770446421 | 9.226880965477186 | 6.207094996728933 | 2.500944375720184 |
| user_004 | Jogging | 1.446046326444e12 | 3.0279 | -8.27659 | 3.21831 | 6.009921272727274 | -4.468944545454545 | -0.8819659999999998 | 9.16817841 | 68.5019420281 | 10.357519256099998 | 9.38230460463739 | 11.679075659240123 | 2.982021272727274 | 3.8076454545454554 | 4.100275999999999 | 7.1263116528925625 | 5.702759008264464 | 2.0436455454545452 | 8.892450870997992 | 14.498163907520668 | 16.812263276175994 | 77.13632860967306 | 50.43628684576454 | 6.854830669867261 | 8.782728995572677 | 7.101850945054011 | 2.6181731550581717 |
| user_004 | Jogging | 1.446046327027e12 | -11.95534 | 3.33447 | -0.652044 | 7.8283936363636375 | -5.684742636363636 | -1.035247090909091 | 142.93015451559998 | 11.1186901809 | 0.42516137793599995 | 12.428757221638694 | 13.073047165181565 | 19.783733636363635 | 9.019212636363637 | 0.383203090909091 | 7.583939752066116 | 5.743929752066115 | 1.863444553719008 | 391.3961165945859 | 81.3461965799415 | 0.14684460888228107 | 84.7339938268665 | 47.89733585550444 | 4.888394068814038 | 9.205106942717531 | 6.920790117862587 | 2.210971295339231 |
| user_004 | Jogging | 1.446046327481e12 | 2.14654 | 5.0972 | -0.345482 | 7.354615454545454 | -5.2457994545454545 | -0.502246909090909 | 4.607633971599999 | 25.98144784 | 0.119357812324 | 5.541519613240037 | 13.876676857035937 | 5.208075454545454 | 10.342999454545454 | 0.15676490909090895 | 8.180594925619834 | 5.740446297520661 | 3.169501487603306 | 27.124049940238834 | 106.97763771672757 | 2.457523672228095e-2 | 92.56675458972168 | 48.67282173154718 | 15.583841577487318 | 9.621161810806514 | 6.9765909821020164 | 3.9476374678391273 |
| user_004 | Jogging | 1.446046327804e12 | -5.17264 | -0.459245 | -0.268841 | 8.615702454545454 | -5.482688636363637 | -1.390581 | 26.756204569600005 | 0.210905970025 | 7.2275483281e-2 | 5.199940963405835 | 12.93261929312298 | 13.788342454545454 | 5.023443636363637 | 1.12174 | 6.679451371900826 | 5.460486388429753 | 2.7520947190082645 | 190.11838764382054 | 25.234985967722317 | 1.2583006276 | 58.47676565362632 | 42.53433048268549 | 11.850820053741382 | 7.647010242809037 | 6.5218349015200845 | 3.4425020049001254 |
| user_004 | Jogging | 1.446046328193e12 | 4.29247 | -6.74378 | -3.41111 | 7.511379727272726 | -5.879826818181818 | -0.561469090909091 | 18.425298700899997 | 45.4785686884 | 11.635671432099999 | 8.691348504196572 | 13.72575512172113 | 3.2189097272727265 | 0.8639531818181823 | 2.8496409090909087 | 7.710933487603305 | 5.303720909090909 | 2.9934193884297526 | 10.361379832330979 | 0.7464151003737611 | 8.12045331076446 | 84.34857147036209 | 41.62065769902936 | 12.905406871537634 | 9.184147835828977 | 6.451407420015369 | 3.592409619118849 |
| user_004 | Jogging | 1.446046328517e12 | 13.02951 | -4.59784 | -1.45677 | 8.302171818181817 | -5.517527272727272 | -0.6555282727272727 | 169.7681308401 | 21.140132665599996 | 2.1221788328999995 | 13.893539589989299 | 13.923873828438145 | 4.727338181818183 | 0.9196872727272725 | 0.8012417272727272 | 6.949278479338843 | 5.963083636363636 | 2.9775846363636362 | 22.347726285276046 | 0.8458246796165284 | 0.6419883055229835 | 67.73211738082999 | 51.18097953516217 | 12.185358501479174 | 8.229952453133006 | 7.1540883091531775 | 3.490753285679063 |
| user_004 | Jogging | 1.446046328711e12 | 10.30876 | -6.39889 | -4.17751 | 6.793744545454545 | -4.280825454545455 | -0.8436463636363635 | 106.27053273759999 | 40.945793232099994 | 17.4515898001 | 12.832299706981598 | 12.584073317230303 | 3.5150154545454546 | 2.118064545454545 | 3.3338636363636365 | 7.7533713471074375 | 5.513691611570248 | 2.685272330578513 | 12.355333645693388 | 4.486197418711568 | 11.11464674586777 | 84.63114349725714 | 45.01627098571577 | 10.222967980701204 | 9.19951865573722 | 6.709416590562533 | 3.197337639458993 |
| user_004 | Jogging | 1.446046328775e12 | 13.02951 | -13.18159 | 2.56686 | 7.246621818181817 | -4.681447272727273 | -1.0979545454545454 | 169.7681308401 | 173.7543149281 | 6.5887702596 | 18.711259071152853 | 13.097908141077136 | 5.782888181818183 | 8.500142727272728 | 3.6648145454545453 | 8.083053553719008 | 5.9361653719008265 | 2.462000578512397 | 33.44179572341242 | 72.25242638400745 | 13.430865652575205 | 87.2485802390777 | 50.235120648544374 | 8.038115823601757 | 9.340694847765754 | 7.08767385314423 | 2.835157107393126 |
| user_004 | Jogging | 1.446046329099e12 | 2.72134 | 5.51872 | -1.57173 | 6.4140263636363635 | -4.204185454545454 | -0.38380181818181813 | 7.405691395600001 | 30.4562704384 | 2.4703351929000004 | 6.350771372589318 | 12.235529986824947 | 3.6926863636363634 | 9.722905454545455 | 1.187928181818182 | 7.503815041322315 | 5.289469958677685 | 2.57411114876033 | 13.635932580185948 | 94.53489047802975 | 1.4111733651578515 | 82.5461956986643 | 43.85794114087185 | 9.103970994478788 | 9.085493695923425 | 6.622532834261515 | 3.017278739937493 |
| user_004 | Jogging | 1.446046329423e12 | -13.48815 | 6.70665 | 2.75846 | 6.807680909090909 | -4.754605454545455 | -0.411670909090909 | 181.93019042249998 | 44.9791542225 | 7.609101571599999 | 15.313995109591747 | 12.947341075284806 | 20.29583090909091 | 11.461255454545455 | 3.1701309090909087 | 7.943389752066116 | 5.935847603305786 | 2.3055521487603303 | 411.92075229040995 | 131.36037659434794 | 10.04972998077355 | 90.62691328474637 | 53.149538307322466 | 7.8468815671253935 | 9.519816872437534 | 7.29037298821689 | 2.801228581734342 |
| user_004 | Jogging | 1.446046329747e12 | 6.63001 | -6.55217 | -2.52974 | 7.490478181818181 | -5.7927363636363625 | -0.5579845454545453 | 43.95703260010001 | 42.930931708900005 | 6.3995844675999995 | 9.658547964192133 | 13.755283506610226 | 0.860468181818181 | 0.7594336363636378 | 1.9717554545454545 | 7.370484462809916 | 5.440533140495867 | 2.5389578512396693 | 0.7404054919214862 | 0.576739448040498 | 3.887819572529752 | 86.65650687937087 | 45.48921107354072 | 10.158724405702777 | 9.308947678409782 | 6.744569005766101 | 3.1872753890592476 |
| user_004 | Jogging | 1.446046331042e12 | 5.36544 | -5.44089 | -3.29615 | 6.971412818181818 | -4.611773636363637 | -1.4254168181818185 | 28.787946393600006 | 29.603283992099996 | 10.864604822499999 | 8.322009084842433 | 12.189372604813066 | 1.6059728181818178 | 0.8291163636363628 | 1.8707331818181814 | 7.043021834710743 | 5.3205052892561975 | 1.757984123966942 | 2.57914869273885 | 0.6874339444495853 | 3.499642637555577 | 80.72587526909831 | 45.50673615751014 | 4.841566405525911 | 8.984757941597442 | 6.745868080351864 | 2.2003559724567094 |
| user_004 | Jogging | 1.446046331301e12 | 8.62267 | -8.08499 | -2.14654 | 7.929420090909091 | -5.778801818181818 | -0.7321687272727274 | 74.35043792889999 | 65.36706330009999 | 4.607633971599999 | 12.013539661590167 | 13.457641481202769 | 0.6932499090909081 | 2.3061881818181815 | 1.4143712727272724 | 6.823233702479339 | 5.110853140495868 | 2.27198279338843 | 0.4805954364545524 | 5.31850392995785 | 2.000446097116164 | 78.5674392359401 | 42.57444091505011 | 7.554090073221997 | 8.863827572552395 | 6.524909264890211 | 2.74847049706232 |
| user_004 | Jogging | 1.446046331366e12 | 2.41478 | 1.91661 | -2.56806 | 7.877165545454544 | -6.092331818181818 | -0.7879078181818183 | 5.8311624484 | 3.6733938920999996 | 6.5949321636 | 4.012416790925389 | 13.236432239904314 | 5.462385545454545 | 8.008941818181817 | 1.7801521818181816 | 6.9201424710743815 | 4.9287523140495875 | 2.3429231487603306 | 29.837655847190746 | 64.14314904702148 | 3.168941790432032 | 79.52284094580611 | 39.29280324603133 | 7.751301455787282 | 8.91755801471491 | 6.2683971831746055 | 2.7841159199622565 |
| user_004 | Jogging | 1.446046331431e12 | 3.37279 | 5.97857 | -0.996927 | 7.305844636363634 | -4.664029090909091 | -0.7948751818181818 | 11.375712384100002 | 35.7432992449 | 0.993863443329 | 6.936344503578884 | 12.617753455719923 | 3.933054636363634 | 10.64259909090909 | 0.20205181818181817 | 6.99963297520661 | 5.3638941322314055 | 2.3321552892561983 | 15.468918772621478 | 113.264915409819 | 4.0824937230578506e-2 | 80.07861579473527 | 46.472050661991666 | 7.7456747098245025 | 8.948665587378672 | 6.817041195562168 | 2.783105227946745 |
| user_004 | Jogging | 1.44604633195e12 | 1.26517 | -6.62882 | 4.09967 | 5.915862727272727 | -5.0019445454545455 | -0.9063518181818183 | 1.6006551288999997 | 43.9412545924 | 16.8072941089 | 7.896151203605463 | 12.055525367001298 | 4.650692727272727 | 1.6268754545454547 | 5.006021818181818 | 7.00184850413223 | 6.076778429752067 | 1.7598838595041322 | 21.628942843507435 | 2.64672374460248 | 25.060254444112395 | 77.09111527379356 | 54.937435054831184 | 5.864779279585238 | 8.780154626986564 | 7.411979159093149 | 2.421730637289217 |
| user_004 | Jogging | 1.446046332532e12 | -12.91335 | 6.32345 | 2.56686 | 7.016699090909091 | -5.4408854545454535 | -0.6346270909090911 | 166.7546082225 | 39.986019902500004 | 6.5887702596 | 14.605800162421778 | 13.1229795705556 | 19.93004909090909 | 11.764335454545453 | 3.201487090909091 | 7.725817520661158 | 5.990320991735536 | 1.863444090909091 | 397.20685676604626 | 138.39958868707515 | 10.249519593257554 | 85.67383220578002 | 55.77066896003064 | 5.5127307761822175 | 9.256016000730552 | 7.467976229208998 | 2.347920521691954 |
| user_004 | Jogging | 1.44604633454e12 | 1.07357 | 1.76333 | -1.87829 | 7.535765454545455 | -6.231677818181819 | -0.5092127272727273 | 1.1525525448999998 | 3.1093326889000004 | 3.5279733241 | 2.7910318088298456 | 13.807935746420464 | 6.462195454545455 | 7.9950078181818185 | 1.3690772727272726 | 7.731201818181817 | 5.40981482644628 | 3.0716415289256203 | 41.75997009274794 | 63.920150012788405 | 1.874372578698347 | 85.4124436525303 | 46.33350515713057 | 12.281585867258803 | 9.241885286700452 | 6.806871906913671 | 3.5045093618449363 |
| user_004 | Jumping | 1.446046441012e12 | 0.11556 | -7.6042e-2 | 0.229323 | 3.066224636363637 | -2.347390636363636 | -1.0352460909090908 | 1.3354113599999998e-2 | 5.782385764e-3 | 5.2589038329e-2 | 0.2678162386656194 | 6.663820669302523 | 2.950664636363637 | 2.2713486363636357 | 1.2645690909090908 | 3.4318280515741835 | 4.729928820297126 | 1.460011539288994 | 8.706421796286955 | 5.159024627910948 | 1.5991349856826442 | 30.129378939789817 | 43.53396871999754 | 4.327364124772764 | 5.489023496013641 | 6.598027638620312 | 2.080231747852331 |
| user_004 | Jumping | 1.446046442049e12 | -0.689167 | 2.8363 | -3.06622 | 3.2856952727272737 | -2.413580636363636 | -0.944672090909091 | 0.47495115388899994 | 8.04459769 | 9.4017050884 | 4.233350201942782 | 7.329990664988696 | 3.9748622727272735 | 5.2498806363636366 | 2.121547909090909 | 4.880614909090909 | 5.935214314049587 | 1.7291644628099172 | 15.799530087150627 | 27.56124669606586 | 4.500965530568007 | 34.00485376745251 | 45.978269371149565 | 5.211924030871701 | 5.831368087117508 | 6.780727790668902 | 2.2829638698130337 |
| user_004 | Jumping | 1.446046442438e12 | 19.39068 | -8.54483 | -1.99325 | 7.2849425454545464 | -5.810152818181818 | -1.181561090909091 | 375.9984708624 | 73.01411972889998 | 3.9730455625 | 21.28345921493496 | 11.312772133446702 | 12.105737454545453 | 2.7346771818181814 | 0.811688909090909 | 5.988737355371901 | 5.692940950413223 | 1.6680408677685947 | 146.54887931838462 | 7.478459288757031 | 0.6588388851411898 | 55.298300085495065 | 49.287809343806956 | 4.708620974921443 | 7.436282679235309 | 7.020527711205687 | 2.169935707554821 |
| user_004 | Jumping | 1.446046444381e12 | 0.575403 | 1.68669 | 0.804128 | 3.881401636363636 | -2.943098090909091 | -1.0108614545454546 | 0.331088612409 | 2.8449231561 | 0.646621840384 | 1.955155648252333 | 6.825409119985633 | 3.305998636363636 | 4.629788090909091 | 1.8149894545454546 | 5.655888628099174 | 5.312588950413223 | 1.0774021487603307 | 10.929626983638219 | 21.43493776672365 | 3.2941867201112065 | 39.46488549288111 | 40.36311851017048 | 1.4810578740002074 | 6.282108363669089 | 6.353197502846144 | 1.2169872119296108 |
| user_004 | Jumping | 1.446046444705e12 | -1.72382 | 1.26517 | -1.45677 | 3.4250405454545447 | -2.538992090909091 | -0.8645478181818181 | 2.9715553923999996 | 1.6006551288999997 | 2.1221788328999995 | 2.5873518033309653 | 7.430472593829023 | 5.148860545454545 | 3.8041620909090907 | 0.5922221818181818 | 5.885177429752065 | 5.714477388429751 | 1.623387438016529 | 26.51076491653847 | 14.471649213909826 | 0.3507271126374875 | 41.50646022554733 | 48.4089620683375 | 4.0390436543496975 | 6.442550754596143 | 6.957654925931402 | 2.0097372102714566 |
| user_004 | Jumping | 1.446046445352e12 | 1.61005 | -0.689167 | -0.613724 | 3.421557363636364 | -2.8525229090909097 | -0.6764302727272727 | 2.5922610025 | 0.47495115388899994 | 0.37665714817600005 | 1.8557665005503792 | 6.618484100427451 | 1.811507363636364 | 2.16335590909091 | 6.27062727272727e-2 | 4.69249785123967 | 4.731132884297521 | 1.220549396694215 | 3.2815589285087703 | 4.680108789398557 | 3.9320766393471035e-3 | 32.01619088385683 | 36.76868457832758 | 2.1039822892654296 | 5.658285153989398 | 6.063718708707354 | 1.4505110441721667 |
| user_004 | Jumping | 1.446046445417e12 | 0.230521 | 0.958607 | -0.805325 | 3.5992247272727274 | -2.8803922727272733 | -0.617208 | 5.3139931441e-2 | 0.918927380449 | 0.6485483556249999 | 1.2730340402027749 | 6.499000667415796 | 3.3687037272727274 | 3.838999272727273 | 0.18811699999999998 | 4.530665413223141 | 4.734299900826446 | 1.183812561983471 | 11.348164802141167 | 14.737915416000531 | 3.538800568899999e-2 | 30.637772691638897 | 36.79289059669946 | 2.075315097724658 | 5.535139807777116 | 6.06571435172309 | 1.440595396953863 |
| user_004 | Jumping | 1.446046445934e12 | 0.537083 | 1.38013 | 0.574206 | 3.4215574545454546 | -2.901293363636363 | -0.6938475454545454 | 0.28845814888899995 | 1.9047588169000003 | 0.329712530436 | 1.5883732232145569 | 6.391787495405162 | 2.8844744545454546 | 4.281423363636363 | 1.2680535454545454 | 4.553151033057851 | 4.739684735537191 | 1.0140632314049587 | 8.320192878925297 | 18.33058601869131 | 1.607959794139843 | 30.362919934925173 | 34.44872365056209 | 1.8205224073468378 | 5.510255886519715 | 5.869303506427495 | 1.3492673594758149 |
| user_004 | Jumping | 1.446046446453e12 | 3.8919e-2 | 1.26517 | -1.15021 | 3.5400021818181817 | -2.8002671818181812 | -0.885449909090909 | 1.514688561e-3 | 1.6006551288999997 | 1.3229830441 | 1.710307826550823 | 6.409141424665947 | 3.501083181818182 | 4.065437181818181 | 0.2647600909090909 | 4.330829314049587 | 4.661143809917356 | 1.3735138842975205 | 12.257583446010123 | 16.527779479309753 | 7.009790573819008e-2 | 29.248410111056636 | 32.12759086779462 | 2.7849106541989044 | 5.408179925913767 | 5.668120576328156 | 1.668805157649899 |
| user_004 | Jumping | 1.446046446647e12 | 14.524 | -16.51545 | -2.95126 | 6.344352909090909 | -5.663839181818181 | -1.2756198181818181 | 210.94657599999996 | 272.76008870250007 | 8.7099355876 | 22.190461921512586 | 10.221041190898104 | 8.17964709090909 | 10.85161081818182 | 1.6756401818181819 | 5.734112421487603 | 6.297515892561984 | 1.6347882066115702 | 66.90662653181752 | 117.75745734928071 | 2.8077700189236694 | 47.85472337486385 | 53.796944082781586 | 3.402840660552115 | 6.917710847879077 | 7.334640010442339 | 1.8446790128778814 |
| user_004 | Jumping | 1.446046447294e12 | 0.690364 | 6.13185 | -2.41478 | 4.372599 | -3.9324581818181814 | -0.8088090909090909 | 0.476602452496 | 37.5995844225 | 5.8311624484 | 6.626262092869252 | 9.25374689473398 | 3.6822350000000004 | 10.06430818181818 | 1.605970909090909 | 5.767999487603306 | 6.21485658677686 | 1.7959872231404963 | 13.558854595225002 | 101.29029917861237 | 2.5791425608462806 | 46.44946369914939 | 57.41211919025554 | 6.140247961835453 | 6.815384339796942 | 7.577078539269309 | 2.477952372794008 |
| user_004 | Jumping | 1.446046447553e12 | -0.305964 | 0.15388 | 0.919089 | 3.3518848181818184 | -2.8490389090909094 | -0.7321676363636364 | 9.361396929600001e-2 | 2.3679054399999996e-2 | 0.8447245899210001 | 0.9808249658410007 | 6.6372655762031405 | 3.6578488181818183 | 3.0029189090909094 | 1.6512566363636365 | 4.801757578512397 | 4.826141876033057 | 1.389348479338843 | 13.379857976674124 | 9.017521974575738 | 2.726648479134951 | 36.44454048917954 | 40.37524681559476 | 3.9165839136820506 | 6.036931380194704 | 6.3541519351991225 | 1.979036107220394 |
| user_004 | Jumping | 1.446046447877e12 | -2.0687 | 1.26517 | -2.68302 | 2.752694363636364 | -2.389195181818182 | -0.8227430909090908 | 4.279519690000001 | 1.6006551288999997 | 7.1985963204 | 3.6164583696345796 | 7.513186647834316 | 4.821394363636364 | 3.6543651818181817 | 1.860276909090909 | 5.199528041322314 | 5.300553785123967 | 2.2431630661157027 | 23.2458436097045 | 13.354384882085032 | 3.4606301784968263 | 35.31507716132555 | 44.82950662438718 | 7.178032604310255 | 5.94264900202978 | 6.695484047056432 | 2.679185063468042 |
| user_004 | Jumping | 1.446046448266e12 | 7.05154 | -11.99366 | 0.765807 | 6.215457636363637 | -6.033109090909092 | -1.5786987272727273 | 49.7242163716 | 143.8478801956 | 0.586460361249 | 13.934078976683352 | 11.008782920283485 | 0.836082363636363 | 5.960550909090908 | 2.344505727272727 | 5.663172289256199 | 5.973218016528926 | 2.258681280991735 | 0.6990337187837675 | 35.52816713986445 | 5.49670710521462 | 48.64535340019356 | 54.29968853739611 | 8.110638392927495 | 6.974622097303449 | 7.368832237023455 | 2.8479182560121865 |
| user_004 | Jumping | 1.446046448589e12 | 0.613724 | 1.38013 | -1.30349 | 3.432009090909091 | -2.922197090909091 | -1.035247090909091 | 0.37665714817600005 | 1.9047588169000003 | 1.6990861801000001 | 1.995119581673239 | 6.530676980772938 | 2.818285090909091 | 4.302327090909091 | 0.2682429090909091 | 4.197816942148761 | 4.6481589338842975 | 1.5796834132231403 | 7.942730853640462 | 18.51001839717028 | 7.195425827755372e-2 | 30.439595937202203 | 32.74221239276296 | 5.038458929277009 | 5.517209071369527 | 5.7220811242731395 | 2.2446511820942265 |
| user_004 | Jumping | 1.446046451632e12 | 0.383802 | 5.02056 | -2.0699 | 4.059068 | -2.8838754545454544 | -1.0317637272727274 | 0.147303975204 | 25.206022713599996 | 4.28448601 | 5.444062150527306 | 8.16787770960744 | 3.6752659999999997 | 7.904435454545454 | 1.0381362727272727 | 5.191295008264462 | 5.4522509917355375 | 1.6376389090909091 | 13.507580170755999 | 62.480099855075196 | 1.0777269207520743 | 41.150174406921245 | 43.123580354647196 | 3.595734975028297 | 6.414840170021483 | 6.5668546774424055 | 1.8962423302490368 |
| user_004 | Jumping | 1.446046451892e12 | -0.382604 | 0.690364 | 7.6042e-2 | 3.557419909090909 | -2.660921272727273 | -1.2129147272727272 | 0.146385820816 | 0.476602452496 | 5.782385764e-3 | 0.7929506031752546 | 6.9635497729535 | 3.9400239090909093 | 3.3512852727272726 | 1.2889567272727271 | 5.185593942148761 | 4.676977388429751 | 1.325692917355372 | 15.523788404208009 | 11.23111297919871 | 1.6614094447816194 | 39.5295646746675 | 36.15230679208021 | 2.620653828008208 | 6.287254144272164 | 6.0126788365985595 | 1.6188433611712432 |
| user_004 | Jumping | 1.44604645228e12 | -1.95374 | -1.72382 | 0.919089 | 3.003517363636363 | -2.389195818181818 | -1.2338168181818183 | 3.8170999876000002 | 2.9715553923999996 | 0.8447245899210001 | 2.7628572112798375 | 7.240861773271672 | 4.957257363636363 | 0.6653758181818179 | 2.1529058181818184 | 4.876497239669423 | 5.02154452892562 | 1.697495950413223 | 24.574400569326944 | 0.4427249794211236 | 4.635003461961125 | 32.368275452791906 | 40.1732580945433 | 5.756142783581481 | 5.689312388399138 | 6.338237775166162 | 2.39919627866948 |
| user_004 | Jumping | 1.446046452538e12 | 18.54763 | -15.40417 | -3.98591 | 4.9648212727272725 | -3.9847141818181813 | -1.3940644545454548 | 344.01457861690005 | 237.28845338890002 | 15.8874785281 | 24.437481673321006 | 8.936126658691224 | 13.58280872727273 | 11.41945581818182 | 2.5918455454545453 | 5.416781504132231 | 5.744247553719009 | 1.6895778347107437 | 184.49269292167622 | 130.4039711834066 | 6.717663331492569 | 45.55107155931277 | 48.760047922413534 | 5.977856426539219 | 6.749153395746222 | 6.982839531480981 | 2.444965526656607 |
| user_004 | Jumping | 1.446046453251e12 | -1.83878 | -3.33327 | 4.02303 | 5.341056181818182 | -4.782472272727273 | -0.7879082727272727 | 3.3811118884000004 | 11.1106888929 | 16.1847703809 | 5.538643440608901 | 10.00383736993665 | 7.179836181818182 | 1.4492022727272729 | 4.810938272727273 | 5.844640479338843 | 5.239432685950413 | 1.737398876033058 | 51.55004759774549 | 2.100187227277893 | 23.145127063992078 | 47.490441190720986 | 50.018904538781086 | 5.802457122441708 | 6.891330872242385 | 7.072404438292615 | 2.4088289940221386 |
| user_004 | Jumping | 1.446046453445e12 | 3.0279 | -1.45557 | -7.7239e-2 | 3.675864363636364 | -2.6922731818181824 | -1.1362745454545455 | 9.16817841 | 2.1186840249000003 | 5.965863121e-3 | 3.3604803671530354 | 7.688581707506595 | 0.6479643636363641 | 1.2367031818181824 | 1.0590355454545455 | 5.11655426446281 | 4.447690247933884 | 1.4346366611570247 | 0.4198578165426783 | 1.5294347599192164 | 1.1215562865362068 | 38.29682912226736 | 35.61574287410279 | 4.090531801082638 | 6.1884431905179 | 5.967892666101058 | 2.0225063166978337 |
| user_004 | Jumping | 1.446046454158e12 | 0.652044 | 1.72501 | -1.03525 | 2.9965505454545447 | -2.4797716363636364 | -1.2582024545454547 | 0.42516137793599995 | 2.9756595001 | 1.0717425625 | 2.114843597180652 | 6.536228248549308 | 2.3445065454545446 | 4.204781636363636 | 0.22295245454545465 | 4.096157082644628 | 4.746651454545454 | 1.610085991735537 | 5.496710941679202 | 17.680188609500856 | 4.970779698784302e-2 | 26.08166873535217 | 35.08718337156006 | 6.381092379007582 | 5.107021513108416 | 5.923443539999353 | 2.5260824173030425 |
| user_004 | Jumping | 1.446046454675e12 | 0.345482 | 2.4531 | -1.99325 | 3.397172363636363 | -2.6365361818181823 | -1.1815614545454545 | 0.119357812324 | 6.01769961 | 3.9730455625 | 3.1796388135799325 | 7.349200198128122 | 3.051690363636363 | 5.089636181818182 | 0.8116885454545455 | 4.695980223140495 | 5.221379603305784 | 1.5765160165289254 | 9.312814075511037 | 25.904396463272764 | 0.6588382948221158 | 35.95807892190684 | 44.31499999041549 | 5.402094381806359 | 5.9965055592325465 | 6.656951253420404 | 2.3242406032522447 |
| user_004 | Jumping | 1.44604645487e12 | 12.22478 | -14.1396 | -1.03525 | 6.717105181818181 | -6.022658545454546 | -1.592634181818182 | 149.4452460484 | 199.92828816 | 1.0717425625 | 18.720183673535363 | 11.750544288016998 | 5.50767481818182 | 8.116941454545454 | 0.557384181818182 | 6.046375016528926 | 6.576525785123966 | 1.9524353471074383 | 30.334481902834142 | 65.88473857651847 | 0.3106771261411241 | 55.06290521812116 | 67.83194182478591 | 6.36341218656381 | 7.420438344068439 | 8.236014923783584 | 2.522580461861189 |
| user_004 | Jumping | 1.44604645597e12 | 8.77595 | -13.18159 | 0.804128 | 6.455829454545454 | -6.026142454545454 | -1.568247545454546 | 77.0172984025 | 173.7543149281 | 0.646621840384 | 15.85617340883304 | 11.218761559924888 | 2.3201205454545457 | 7.155447545454546 | 2.372375545454546 | 5.748047107438017 | 5.903544363636365 | 2.0901981487603303 | 5.382959345440299 | 51.200429575751485 | 5.628165728670754 | 50.331598154741954 | 56.07913702398294 | 6.685448936200232 | 7.094476594840662 | 7.488600471649088 | 2.585623510142231 |
| user_004 | Jumping | 1.446046456165e12 | -2.03038 | 0.383802 | 0.344284 | 3.4389759090909084 | -1.9363195454545457 | -1.3766462727272726 | 4.1224429444 | 0.147303975204 | 0.11853147265599999 | 2.094821804416786 | 7.445358468937255 | 5.4693559090909085 | 2.320121545454546 | 1.7209302727272726 | 4.207634471074379 | 5.526991512396695 | 1.8906791074380165 | 29.91385406030764 | 5.38296398568239 | 2.961601003589165 | 28.717641389713194 | 45.835480733710945 | 5.353617666866859 | 5.35888434188621 | 6.77019059803422 | 2.3137885959756264 |
| user_004 | Jumping | 1.446046456553e12 | -3.7722e-2 | -8.19995 | 4.44456 | 5.818319 | -5.827572363636364 | -1.2756197272727272 | 1.422949284e-3 | 67.23918000249999 | 19.7541135936 | 9.327095825892645 | 10.682069515574172 | 5.856040999999999 | 2.3723776363636357 | 5.720179727272727 | 6.01913994214876 | 6.165136165289257 | 2.8100497024793394 | 34.29321619368099 | 5.628175649518311 | 32.72045611230189 | 52.990159518859336 | 53.34973957557859 | 10.45758717127323 | 7.279434010887065 | 7.304090605652328 | 3.233819285500232 |
| user_004 | Standing | 1.446046405273e12 | 1.03525 | -9.34956 | 0.152682 | 1.0666006363636364 | -9.363494545454545 | 0.1875190909090909 | 1.0717425625 | 87.41427219360001 | 2.3311793124000002e-2 | 9.407939548552807 | 9.43353814467717 | 3.135063636363644e-2 | 1.3934545454544534e-2 | 3.48370909090909e-2 | 0.16144091391512527 | 5.332828964974403e-2 | 0.1500458602879444 | 9.828624004049637e-4 | 1.941715570247677e-4 | 1.2136229030082638e-3 | 5.7123950662914674e-2 | 6.371192410501708e-3 | 3.864845095038317e-2 | 0.23900617285525216 | 7.981974950162214e-2 | 0.1965920927972007 |
| user_004 | Standing | 1.446046405402e12 | 0.958607 | -9.38788 | 0.152682 | 1.0073788181818184 | -9.370461818181816 | 0.239774 | 0.918927380449 | 88.13229089439999 | 2.3311793124000002e-2 | 9.437930391138355 | 9.428698068400793 | 4.8771818181818416e-2 | 1.7418181818182887e-2 | 8.709199999999997e-2 | 7.313571143578647e-2 | 2.904839984258151e-2 | 0.10803079004001047 | 2.3786902487603536e-3 | 3.0339305785127694e-4 | 7.5850164639999955e-3 | 7.137901634508789e-3 | 1.51052916609067e-3 | 1.8586924123821767e-2 | 8.448610320347831e-2 | 3.886552670543228e-2 | 0.13633387005370956 |
| user_004 | Standing | 1.446046406114e12 | 1.03525 | -9.38788 | -0.11556 | 0.9760256363636363 | -9.387879999999997 | 2.7270636363636358e-2 | 1.0717425625 | 88.13229089439999 | 1.3354113599999998e-2 | 9.445495623338141 | 9.439062086037746 | 5.922436363636374e-2 | 1.7763568394002505e-15 | 0.14283063636363635 | 5.573941322314055e-2 | 3.0085950413224018e-2 | 9.37422396694215e-2 | 3.507525248132244e-3 | 3.1554436208840472e-30 | 2.040059068404132e-2 | 4.640469144164543e-3 | 1.4684224000000342e-3 | 1.0355239224131481e-2 | 6.812098901340571e-2 | 3.8320000000000444e-2 | 0.10176069587090823 |
| user_004 | Standing | 1.44604640689e12 | 1.11189 | -9.38788 | -7.7239e-2 | 1.0352489090909094 | -9.380912727272726 | -7.723927272727273e-2 | 1.2362993721000002 | 88.13229089439999 | 5.965863121e-3 | 9.453811724887533 | 9.438361848585513 | 7.664109090909066e-2 | 6.967272727273155e-3 | 2.7272727272376063e-7 | 3.642103305785122e-2 | 4.0536859504131814e-2 | 3.1986380165289256e-2 | 5.8738568157354985e-3 | 4.854288925620431e-5 | 7.438016528734051e-14 | 2.051035420896313e-3 | 2.107202692712216e-3 | 1.7729483020330575e-3 | 4.528835855820249e-2 | 4.590427749907645e-2 | 4.2106392650440354e-2 |
| user_004 | Standing | 1.446046408768e12 | 1.07357 | -9.27292 | -0.11556 | 1.0561512727272726 | -9.373945454545451 | -5.6337272727272736e-2 | 1.1525525448999998 | 85.98704532639998 | 1.3354113599999998e-2 | 9.335574539625291 | 9.433578337867402 | 1.7418727272727308e-2 | 0.10102545454545186 | 5.922272727272726e-2 | 3.135386776859501e-2 | 4.338710743801661e-2 | 3.4520033057851236e-2 | 3.034120598016541e-4 | 1.020614246611516e-2 | 3.507331425619833e-3 | 1.3670026915131445e-3 | 2.5804958629601416e-3 | 1.5500973776108185e-3 | 3.697299949305093e-2 | 5.0798581308537955e-2 | 3.9371276047530115e-2 |
| user_004 | Standing | 1.446046409157e12 | 1.03525 | -9.38788 | -0.11556 | 1.035248909090909 | -9.373945454545453 | -9.465772727272728e-2 | 1.0717425625 | 88.13229089439999 | 1.3354113599999998e-2 | 9.445495623338141 | 9.43151731218211 | 1.090909091061576e-6 | 1.393454545454631e-2 | 2.0902272727272714e-2 | 2.6919925619834695e-2 | 4.2120330578512076e-2 | 3.135312396694215e-2 | 1.190082644960794e-12 | 1.9417155702481723e-4 | 4.369050051652887e-4 | 1.1242853090751301e-3 | 2.3708788411720117e-3 | 1.3051776456258447e-3 | 3.353036398661861e-2 | 4.869167116840427e-2 | 3.612724243041315e-2 |
| user_004 | Standing | 1.446046410129e12 | 1.11189 | -9.38788 | 3.7722e-2 | 1.1084063636363637 | -9.349559999999999 | -0.12601072727272725 | 1.2362993721000002 | 88.13229089439999 | 1.422949284e-3 | 9.453571452936927 | 9.4163566506059 | 3.4836363636363554e-3 | 3.8320000000000576e-2 | 0.16373272727272725 | 3.736996694214872e-2 | 6.2072066115702255e-2 | 6.2072644628099166e-2 | 1.213572231404953e-5 | 1.4684224000000442e-3 | 2.6808405980165283e-2 | 2.1358955917129977e-3 | 4.743964177310282e-3 | 6.979293404045078e-3 | 4.6215750472246984e-2 | 6.887644138099966e-2 | 8.354216542587987e-2 |
| user_004 | Standing | 1.446046410257e12 | 0.537083 | -9.54116 | -0.11556 | 1.0108632727272726 | -9.373945454545456 | -0.10510872727272727 | 0.28845814888899995 | 91.0337341456 | 1.3354113599999998e-2 | 9.556963241955522 | 9.432190682868356 | 0.47378027272727263 | 0.16721454545454328 | 1.0451272727272726e-2 | 0.12129503305785122 | 7.600661157024775e-2 | 6.999014049586777e-2 | 0.22446774682552884 | 2.7960704211569522e-2 | 1.0922910161983468e-4 | 4.670707975050865e-2 | 7.85070908970695e-3 | 8.712536485643125e-3 | 0.21611820781810276 | 8.860422726770405e-2 | 9.334096895599019e-2 |
| user_004 | Standing | 1.446046410451e12 | 1.18853 | -9.31124 | -0.230521 | 1.0317648181818182 | -9.373945454545455 | -0.12252709090909089 | 1.4126035609000003 | 86.6991903376 | 5.3139931441e-2 | 9.389618407046209 | 9.43526488450923 | 0.1567651818181819 | 6.270545454545484e-2 | 0.10799390909090911 | 0.15454828925619835 | 7.695669421487591e-2 | 8.86753388429752e-2 | 2.4575322230487626e-2 | 3.931974029752103e-3 | 1.1662684400735542e-2 | 5.5891736600888815e-2 | 8.065842348910572e-3 | 1.1085674047815176e-2 | 0.23641433247772609 | 8.981003478960785e-2 | 0.10528852761728211 |
| user_004 | Standing | 1.446046412718e12 | 1.18853 | -9.34956 | -0.422122 | 1.1049214545454544 | -9.353043636363635 | -0.122527 | 1.4126035609000003 | 87.41427219360001 | 0.178186982884 | 9.434249452785526 | 9.422764007092963 | 8.360854545454566e-2 | 3.483636363634801e-3 | 0.299595 | 0.1573988347107438 | 6.492231404958672e-2 | 0.16341548760330576 | 6.990388873024827e-3 | 1.21357223140387e-5 | 8.9757164025e-2 | 3.3189741033637116e-2 | 5.969672131029308e-3 | 3.4876410140268974e-2 | 0.18218051771151908 | 7.726365336320376e-2 | 0.1867522694380686 |
| user_004 | Standing | 1.446046413041e12 | 1.07357 | -9.34956 | 0.191003 | 1.028281 | -9.398330909090909 | -7.375563636363637e-2 | 1.1525525448999998 | 87.41427219360001 | 3.6482146009e-2 | 9.412932958674944 | 9.46146923115552 | 4.528899999999991e-2 | 4.877090909090853e-2 | 0.2647586363636364 | 0.16278274380165286 | 7.220628099173544e-2 | 0.23910598347107437 | 2.051093520999992e-3 | 2.3786015735536644e-3 | 7.009713552913224e-2 | 3.950375507351463e-2 | 9.099585240571058e-3 | 8.419125594356573e-2 | 0.19875551583167356 | 9.539174618682195e-2 | 0.29015729517550604 |
| user_004 | Standing | 1.446046413623e12 | 1.18853 | -9.69444 | -0.613724 | 1.3487772727272729 | -9.286854545454545 | -0.4325731818181819 | 1.4126035609000003 | 93.9821669136 | 0.37665714817600005 | 9.786287734512817 | 9.404364206633495 | 0.1602472727272728 | 0.4075854545454547 | 0.18115081818181816 | 0.19540091735537188 | 0.13649520661157022 | 0.3477331074380165 | 2.5679188416528945e-2 | 0.1661259027570249 | 3.2815618927942145e-2 | 6.511500802742975e-2 | 3.193570489316307e-2 | 0.14773169252623738 | 0.2551764252971456 | 0.17870563755282895 | 0.3843588070101131 |
| user_004 | Standing | 1.446046413883e12 | 1.26517 | -9.4262 | -0.575403 | 1.3940647272727273 | -9.286853636363638 | -0.5057301818181817 | 1.6006551288999997 | 88.85324643999999 | 0.331088612409 | 9.528115772874981 | 9.4180607748967 | 0.12889472727272744 | 0.13934636363636166 | 6.967281818181825e-2 | 0.30814510743801654 | 0.16721545454545475 | 0.34678295041322316 | 1.6613850718710788e-2 | 1.9417409058677136e-2 | 4.854301593396704e-3 | 0.16176828405792787 | 3.9379707218407264e-2 | 0.17025261915338166 | 0.40220428150123894 | 0.19844320905086993 | 0.41261679456049977 |
| user_004 | Standing | 1.446046413947e12 | 1.03525 | -9.4262 | -0.767005 | 1.3557447272727272 | -9.318206363636364 | -0.5022465454545454 | 1.0717425625 | 88.85324643999999 | 0.5882966700250001 | 9.513847049039889 | 9.443627716736785 | 0.3204947272727272 | 0.10799363636363601 | 0.26475845454545466 | 0.31384557851239675 | 0.15391438016528947 | 0.3135297933884298 | 0.1027168702096198 | 1.1662625495041247e-2 | 7.009703925329758e-2 | 0.16506477682737863 | 3.4560740062359194e-2 | 0.1404810132548272 | 0.4062816471702588 | 0.18590519105812833 | 0.37480796850497616 |
| user_004 | Standing | 1.446046415048e12 | 1.18853 | -9.31124 | -0.805325 | 1.2582025454545456 | -9.360010909090908 | -0.4987628181818181 | 1.4126035609000003 | 86.6991903376 | 0.6485483556249999 | 9.421270734573175 | 9.468674129989651 | 6.967254545454549e-2 | 4.877090909090853e-2 | 0.30656218181818184 | 0.25399059504132226 | 0.11369338842975213 | 0.18083376859504133 | 4.854263590115707e-3 | 2.3786015735536644e-3 | 9.398037132112398e-2 | 0.13511066408620737 | 2.2650802290157788e-2 | 4.892429089689557e-2 | 0.36757402531491173 | 0.1505018348398377 | 0.22118836067229117 |
| user_004 | Standing | 1.446046415501e12 | 1.53341 | -9.34956 | -0.575403 | 1.3418098181818179 | -9.335625454545452 | -0.5475341818181818 | 2.3513462280999997 | 87.41427219360001 | 0.331088612409 | 9.491928520280219 | 9.460269131051309 | 0.19160018181818206 | 1.3934545454548086e-2 | 2.786881818181819e-2 | 0.349632305785124 | 8.424066115702528e-2 | 0.1729163966942149 | 3.6710629672760425e-2 | 1.9417155702486672e-4 | 7.7667102685124e-4 | 0.1744459141085838 | 1.6734057823591317e-2 | 4.259708688935688e-2 | 0.41766722891386127 | 0.1293601863928439 | 0.20639061725126187 |
| user_004 | Standing | 1.446046415631e12 | 0.690364 | -9.34956 | -0.345482 | 1.421934 | -9.30078909090909 | -0.5092138181818181 | 0.476602452496 | 87.41427219360001 | 0.119357812324 | 9.381376895659827 | 9.431691846432294 | 0.73157 | 4.877090909091031e-2 | 0.1637318181818181 | 0.309728520661157 | 5.257123966942213e-2 | 0.17671676033057848 | 0.5351946649000001 | 2.378601573553838e-3 | 2.6808108285123936e-2 | 0.1570628366335545 | 5.015363058151769e-3 | 4.53707014452577e-2 | 0.39631153986927314 | 7.081922802566948e-2 | 0.2130039939655069 |
| user_004 | Standing | 1.446046416019e12 | 1.41845 | -9.27292 | -0.383802 | 1.195496181818182 | -9.33910909090909 | -0.39425290909090904 | 2.0120004025 | 85.98704532639998 | 0.147303975204 | 9.388628744609298 | 9.428671946840348 | 0.22295381818181803 | 6.618909090909142e-2 | 1.0450909090909066e-2 | 0.22358721487603306 | 5.9538512396694804e-2 | 0.1029263636363636 | 4.970840504185117e-2 | 4.3809957553719685e-3 | 1.0922150082644576e-4 | 0.10089118967844403 | 9.03339039158533e-3 | 2.0605700858405702e-2 | 0.3176337351076614 | 9.504414969678739e-2 | 0.14354685945155923 |
| user_004 | Standing | 1.446046416926e12 | 0.690364 | -9.2346 | -0.268841 | 1.0944696363636364 | -9.33214090909091 | -0.23748800000000003 | 0.476602452496 | 85.27783716 | 7.2275483281e-2 | 9.264270888514487 | 9.420054247814711 | 0.4041056363636364 | 9.754090909090962e-2 | 3.1352999999999964e-2 | 0.47187835537190087 | 0.17671619834710775 | 0.20015231404958678 | 0.1633013653408595 | 9.514228946281095e-3 | 9.830106089999977e-4 | 0.310273820857556 | 4.999332285725033e-2 | 5.834728167914349e-2 | 0.5570222803959963 | 0.22359186670639505 | 0.24155181986303373 |
| user_004 | Standing | 1.446046417379e12 | 1.57173 | -9.19628 | 3.7722e-2 | 1.2163987272727275 | -9.349559999999999 | -0.3210958181818182 | 2.4703351929000004 | 84.57156583839999 | 1.422949284e-3 | 9.32970117316648 | 9.460635541955476 | 0.3553312727272726 | 0.15327999999999875 | 0.3588178181818182 | 0.5744890165289257 | 9.912528925619846e-2 | 0.1912847520661157 | 0.12626031337798338 | 2.3494758399999618e-2 | 0.12875022664476032 | 0.39191257683704356 | 1.2132424088054128e-2 | 5.2577204522029304e-2 | 0.6260292140443955 | 0.11014728361632041 | 0.22929719693452275 |
| user_004 | Standing | 1.446046417508e12 | 0.422122 | -9.6178 | 0.229323 | 1.1397578181818184 | -9.380912727272728 | -0.27929181818181825 | 0.178186982884 | 92.50207684000002 | 5.2589038329e-2 | 9.629789865890793 | 9.47857549528891 | 0.7176358181818184 | 0.23688727272727306 | 0.5086148181818182 | 0.49974833884297526 | 0.10704264462809929 | 0.2413228429752066 | 0.5150011675374879 | 5.611557998016545e-2 | 0.25868903327412396 | 0.31828234156348995 | 1.5379286037115e-2 | 8.723958200181668e-2 | 0.564165172235481 | 0.1240132494418036 | 0.29536347438675736 |
| user_004 | Standing | 1.446046417833e12 | 1.38013 | -9.34956 | -0.575403 | 1.0944693636363636 | -9.401814545454545 | -0.17826545454545456 | 1.9047588169000003 | 87.41427219360001 | 0.331088612409 | 9.468374708623916 | 9.48262053217955 | 0.2856606363636365 | 5.225454545454511e-2 | 0.39713754545454544 | 0.3860533636363636 | 0.12604429752066115 | 0.3037121074380166 | 8.160199916767776e-2 | 2.730537520661121e-3 | 0.15771823000966115 | 0.1960315428160354 | 2.5699046871224675e-2 | 0.11621021168655223 | 0.4427544949698822 | 0.1603092226642768 | 0.34089618901734914 |
| user_004 | Standing | 1.446046418027e12 | 0.383802 | -9.46452 | -0.1922 | 1.1606589090909092 | -9.366978181818181 | -0.1817491818181818 | 0.147303975204 | 89.5771388304 | 3.694084e-2 | 9.474248447534189 | 9.457782217497279 | 0.7768569090909092 | 9.754181818181884e-2 | 1.04508181818182e-2 | 0.37370195041322324 | 0.14979636363636348 | 0.3331650661157025 | 0.6035066572022811 | 9.514406294215004e-3 | 1.0921960066942186e-4 | 0.18871104882008571 | 3.252925203906836e-2 | 0.15173433473975734 | 0.4344088498408909 | 0.18035867608481818 | 0.3895309162823374 |
| user_004 | Standing | 1.446046418351e12 | 1.45677 | -9.2346 | -0.805325 | 1.390580181818182 | -9.31124 | -0.5196647272727272 | 2.1221788328999995 | 85.27783716 | 0.6485483556249999 | 9.383419651093359 | 9.439115301474105 | 6.61898181818179e-2 | 7.663999999999938e-2 | 0.2856602727272728 | 0.3334811404958678 | 0.11495999999999995 | 0.29041093388429756 | 4.381092030942112e-3 | 5.8736895999999044e-3 | 8.160179141461987e-2 | 0.14857728864795347 | 2.0777459849135996e-2 | 0.11462817207026825 | 0.38545724619982624 | 0.14414388592353128 | 0.3385678249188311 |
| user_004 | Standing | 1.446046418414e12 | 1.49509 | -9.15796 | -0.537083 | 1.380129272727273 | -9.290338181818182 | -0.5440503636363636 | 2.2352941081 | 83.86823136159998 | 0.28845814888899995 | 9.2947288082326 | 9.417841836340516 | 0.1149607272727271 | 0.13237818181818284 | 6.967363636363633e-3 | 0.29611109090909093 | 0.12604429752066115 | 0.2831268925619835 | 1.3215968815074341e-2 | 1.7523983021487874e-2 | 4.854415604132226e-5 | 0.12462338582322618 | 2.236061998737795e-2 | 0.11394304110316159 | 0.35302037593207874 | 0.14953467821003244 | 0.3375545009374954 |
| user_004 | Standing | 1.446046420034e12 | 1.34181 | -9.46452 | -0.575403 | 1.4358680909090908 | -9.314722727272727 | -0.4151547272727273 | 1.8004540760999999 | 89.5771388304 | 0.331088612409 | 9.576464980299829 | 9.460734936625196 | 9.40580909090909e-2 | 0.14979727272727317 | 0.1602482727272727 | 0.39967030578512386 | 0.22802107438016542 | 0.32049712396694213 | 8.846924465462808e-3 | 2.2439222916529056e-2 | 2.5679508912074375e-2 | 0.30807900124276705 | 0.10420531264410249 | 0.21827880078362583 | 0.5550486476361933 | 0.3228084767228124 | 0.4672031686361147 |
| user_004 | Standing | 1.446046420358e12 | 1.49509 | -10.15428 | 0.191003 | 1.1815612727272726 | -9.391362727272728 | -0.2583899090909091 | 2.2352941081 | 103.1094023184 | 3.6482146009e-2 | 10.265533525955142 | 9.482193608129768 | 0.3135287272727274 | 0.7629172727272717 | 0.4493929090909091 | 0.29547742148760325 | 0.2520899173553719 | 0.3854200330578513 | 9.830026282525628e-2 | 0.5820427650256182 | 0.2019539867411901 | 0.1540229967075116 | 0.1056237733646881 | 0.2458130464102584 | 0.39245763683168605 | 0.324998112863272 | 0.4957953674755931 |
| user_004 | Standing | 1.446046420811e12 | 1.03525 | -9.34956 | -0.15388 | 1.0700842727272728 | -9.387878181818182 | -0.13994536363636365 | 1.0717425625 | 87.41427219360001 | 2.3679054399999996e-2 | 9.407959067220691 | 9.457822154203965 | 3.48342727272728e-2 | 3.831818181818214e-2 | 1.3934636363636344e-2 | 0.2017356694214876 | 0.2622251239669419 | 0.21472052892561988 | 1.2134265564380214e-3 | 1.4682830578512643e-3 | 1.941740905867763e-4 | 5.323802926614573e-2 | 0.12122007555927854 | 8.071933674789031e-2 | 0.23073367605563286 | 0.34816673528537806 | 0.2841114864765068 |
| user_004 | Walking | 1.446046366398e12 | -3.98471 | -13.79471 | 0.459245 | -3.98471 | -13.79471 | 0.459245 | 15.877913784100002 | 190.2940239841 | 0.210905970025 | 14.36603089716241 | 14.36603089716241 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_004 | Walking | 1.44604636957e12 | 3.2195 | -12.4535 | -7.7413 | 9.814109090909096e-2 | -9.537674545454545 | -0.9620902727272728 | 10.36518025 | 155.08966225 | 59.927725689999995 | 15.012746856921288 | 10.327146098830262 | 3.121358909090909 | 2.9158254545454554 | 6.779209727272727 | 2.1396030082644626 | 3.036173305785124 | 1.7193464545454544 | 9.74288143936119 | 8.502038081375211 | 45.95768452634916 | 8.013099460562149 | 12.077640196295194 | 6.212826294378195 | 2.830741856927641 | 3.4752899442054033 | 2.492554170801147 |
| user_004 | Walking | 1.446046370218e12 | 1.41845 | -11.64878 | -0.881966 | 0.160846909090909 | -9.941780909090907 | -1.2965218181818183 | 2.0120004025 | 135.69407548840002 | 0.7778640251560001 | 11.76791994857443 | 10.836836010794931 | 1.257603090909091 | 1.7069990909090933 | 0.4145558181818183 | 2.1763401157024793 | 2.037311983471075 | 2.641251256198348 | 1.5815655342640993 | 2.913845896364471 | 0.1718565263883968 | 6.473788210836907 | 6.137056866684374 | 11.89691584533187 | 2.54436400910658 | 2.4773083915177727 | 3.449190607277578 |
| user_004 | Walking | 1.446046371772e12 | -2.2603 | -5.13432 | -4.79064 | -0.49756490909090906 | -9.17189 | -0.4081887272727273 | 5.1089560899999995 | 26.361241862399996 | 22.9502316096 | 7.377020371532127 | 9.669964731495844 | 1.7627350909090909 | 4.03757 | 4.382451272727272 | 1.8675612314049586 | 2.646952809917355 | 1.442553603305785 | 3.1072350007222806 | 16.301971504899996 | 19.20587915782889 | 4.540956214693467 | 10.45240783780894 | 3.4169098988482323 | 2.130951950348357 | 3.2330183788232536 | 1.848488544418989 |
| user_004 | Walking | 1.446046371901e12 | 3.90927 | -8.73643 | -6.89825 | -7.952527272727285e-2 | -9.607348181818182 | -1.8713270909090909 | 15.282391932899998 | 76.3252091449 | 47.5858530625 | 11.79802755295562 | 10.56487874733498 | 3.988795272727273 | 0.8709181818181815 | 5.026922909090909 | 2.214027214876033 | 2.9969032231404955 | 2.1465695867768595 | 15.91048772773144 | 0.758498479421487 | 25.269953933943007 | 5.978305022883947 | 12.236695075020059 | 9.143849650247803 | 2.4450572637228656 | 3.4980987800546828 | 3.0238799001031444 |
| user_004 | Walking | 1.446046372743e12 | 1.45677 | -8.00835 | -0.843646 | -0.1491985454545455 | -10.429494545454544 | 0.6682654545454546 | 2.1221788328999995 | 64.1336697225 | 0.711738573316 | 8.183372601117219 | 10.966827189484333 | 1.6059685454545454 | 2.4211445454545437 | 1.5119114545454546 | 2.2741999008264457 | 1.9518016528925615 | 1.8631270743801656 | 2.579134968989388 | 5.861940909984289 | 2.2858762463857523 | 7.693418660663753 | 6.3853173224877535 | 6.652061767787009 | 2.773701256563827 | 2.5269185429071026 | 2.5791591202923114 |
| user_004 | Walking | 1.44604637352e12 | 0.383802 | -9.73276 | 0.574206 | -0.6996175454545454 | -8.816557272727273 | -1.6274699999999995 | 0.147303975204 | 94.72661721760001 | 0.329712530436 | 9.757234942504972 | 9.870908253729358 | 1.0834195454545452 | 0.9162027272727276 | 2.2016759999999995 | 1.508109520661157 | 3.007987024793388 | 3.2391752975206605 | 1.1737979114729333 | 0.839427437461984 | 4.847377208975998 | 2.9520625124599444 | 15.383085965611043 | 13.087474885912942 | 1.7181567194118075 | 3.922127734484312 | 3.6176615217448056 |
| user_004 | Walking | 1.446046374233e12 | -3.02671 | -14.1396 | -3.10454 | -0.4766623636363638 | -10.004486363636364 | -1.8573914545454544 | 9.1609734241 | 199.92828816 | 9.638168611600001 | 14.78943643942189 | 10.961880383630952 | 2.550047636363636 | 4.135113636363636 | 1.2471485454545457 | 2.1187018842975207 | 3.281294380165289 | 1.8726276446280992 | 6.5027429477237675 | 17.099164785640493 | 1.555379494429389 | 7.455724069051604 | 13.679017013026895 | 8.523442609325427 | 2.730517179775949 | 3.6985155147743933 | 2.9194935535680546 |
| user_004 | Walking | 1.44604637488e12 | -6.62882 | -15.59577 | -1.07357 | -1.0967554545454545 | -10.439943636363637 | 1.681954545454547e-2 | 43.9412545924 | 243.2280418929 | 1.1525525448999998 | 16.980042668680195 | 11.156214455696425 | 5.532064545454546 | 5.155826363636363 | 1.0903895454545454 | 2.5699958016528925 | 2.8414043801652884 | 1.8181555867768593 | 30.603738135075208 | 26.582545491967764 | 1.18894936083657 | 9.199901619889102 | 10.711808598846353 | 5.263144219387184 | 3.0331339600962406 | 3.272889946033376 | 2.2941543582303225 |
| user_004 | Walking | 1.446046377146e12 | -6.13065 | -15.71073 | -0.15388 | -1.1942974545454548 | -10.60367818181818 | 0.30248054545454545 | 37.5848694225 | 246.8270371329 | 2.3679054399999996e-2 | 16.86521822004684 | 11.12039058972711 | 4.936352545454545 | 5.107051818181819 | 0.4563605454545454 | 2.235246099173554 | 2.537375454545454 | 1.7392981074380165 | 24.367576453015566 | 26.081978273594228 | 0.2082649474475702 | 8.021353013049096 | 9.933662612994366 | 4.954402849227404 | 2.8321993243853965 | 3.1517713452905123 | 2.225848792983792 |
| user_004 | Walking | 1.446046378571e12 | 0.268841 | -5.63249 | -6.20849 | -0.7936770909090908 | -9.098734545454548 | -0.7426196363636364 | 7.2275483281e-2 | 31.724943600099998 | 38.545348080100005 | 8.387047583236965 | 9.780550153373028 | 1.0625180909090908 | 3.466244545454548 | 5.465870363636364 | 1.972702834710744 | 2.7495629752066115 | 1.4758074380165291 | 1.128944693509099 | 12.014851248893406 | 29.875738832078323 | 6.444071232003932 | 11.0196444942568 | 4.041404088049947 | 2.538517526432294 | 3.319584988256333 | 2.010324373838696 |
| user_004 | Walking | 1.446046379154e12 | 0.575403 | -9.54116 | 0.535886 | -0.905154090909091 | -8.757334545454546 | -1.428901909090909 | 0.331088612409 | 91.0337341456 | 0.287173804996 | 9.573504925731484 | 9.909833604318145 | 1.480557090909091 | 0.7838254545454539 | 1.964787909090909 | 1.7253638099173552 | 3.7781931404958673 | 3.2147897107438017 | 2.1920492994411904 | 0.6143823431933875 | 3.8603915277098264 | 3.616326826626917 | 19.840932568717125 | 12.920697637420618 | 1.90166422552114 | 4.454316172962706 | 3.594537193773437 |
| user_004 | Walking | 1.446046380187e12 | 0.958607 | -10.07764 | -0.920286 | -1.0967548181818179 | -8.328842727272727 | 0.5637550909090907 | 0.918927380449 | 101.55882796960002 | 0.8469263217960001 | 10.164874897009064 | 8.842630776733644 | 2.055361818181818 | 1.7487972727272734 | 1.4840410909090909 | 1.4982920743801655 | 2.0955819008264474 | 1.9347014214876035 | 4.224512203639668 | 3.0582919010983494 | 2.2023779595066446 | 2.941469055338058 | 4.996719767526751 | 4.5970013811859465 | 1.715071151683818 | 2.2353343748814742 | 2.1440618883758806 |
| user_004 | Walking | 1.446046380316e12 | 4.714 | -10.95901 | 0.497565 | 1.5438635454545455 | -9.690957272727273 | 0.11436227272727267 | 22.221796000000005 | 120.09990018009998 | 0.24757092922499999 | 11.94023731377752 | 10.136048326231052 | 3.170136454545455 | 1.2680527272727264 | 0.3832027272727273 | 2.048077595041322 | 0.9906261983471066 | 1.194263049586777 | 10.049765140438026 | 1.6079577191437995 | 0.14684433018925622 | 5.8591245476648615 | 1.1315468820490597 | 1.8221705993481947 | 2.4205628576149105 | 1.0637419245517494 | 1.349877994245478 |
| user_004 | Walking | 1.446046380422e12 | 0.613724 | -13.29655 | 1.14901 | -0.4940828181818182 | -12.36641090909091 | -0.648560818181818 | 0.37665714817600005 | 176.7982419025 | 1.3202239801000002 | 13.360206698654629 | 12.52145450968992 | 1.1078068181818184 | 0.9301390909090905 | 1.7975708181818182 | 2.1177524628099174 | 1.1195233884297524 | 1.0897538842975207 | 1.2272359464101243 | 0.8651587284371893 | 3.2312608463788512 | 6.189753507877224 | 1.9289718136152516 | 1.5199968912630324 | 2.4879215236572927 | 1.3888742972692856 | 1.2328815398338286 |
| user_004 | Walking | 1.44604638047e12 | 5.99e-4 | -13.67975 | -1.72501 | 0.5649522727272728 | -13.397576363636365 | 0.5114998181818182 | 3.5880100000000004e-7 | 187.13556006250002 | 2.9756595001 | 13.78808253244087 | 13.532747315761641 | 0.5643532727272728 | 0.2821736363636358 | 2.236509818181818 | 1.546431008264463 | 0.9744771074380164 | 1.4080335289256196 | 0.3184946164379835 | 7.962196105867736e-2 | 5.0019761668236695 | 3.1877217617931106 | 1.8049142719133733 | 2.4790139155103645 | 1.7854192117799983 | 1.3434709791853985 | 1.5744884615361157 |
| user_004 | Walking | 1.446046380511e12 | -8.31491 | -15.74905 | -0.383802 | -1.5078282727272723 | -13.819099090909093 | -0.6903636363636364 | 69.1377283081 | 248.0325759025 | 0.147303975204 | 17.813410908240005 | 14.457859256629503 | 6.8070817272727275 | 1.9299509090909073 | 0.3065616363636364 | 3.1866037851239666 | 0.6213592561983465 | 1.687992991735537 | 46.33636164177026 | 3.7247105115008194 | 9.398003688995045e-2 | 13.971892093536734 | 0.8649275025076619 | 3.7915307480236042 | 3.73789942260847 | 0.9300147861768984 | 1.947185339926224 |
| user_004 | Walking | 1.446046380535e12 | -9.38788 | -12.2619 | 2.8351 | -4.5699700909090915 | -14.115210000000001 | -0.6276575454545454 | 88.13229089439999 | 150.35419161000002 | 8.03779201 | 15.701091507102301 | 15.508353656426648 | 4.817909909090908 | 1.8533100000000005 | 3.4627575454545454 | 4.157596636363636 | 0.9010022314049583 | 1.8985960826446282 | 23.212255892116357 | 3.4347579561000017 | 11.990689818602387 | 21.991971959348977 | 1.3288549527057087 | 4.8486276390150325 | 4.689559889728351 | 1.1527597116076311 | 2.2019599539989443 |
| user_004 | Walking | 1.446046380559e12 | -4.7128 | -7.05034 | 1.99206 | -6.621849090909092 | -12.742646363636362 | 0.43485954545454564 | 22.21048384 | 49.70729411560001 | 3.9683030435999997 | 8.711261734054373 | 14.811878511140526 | 1.9090490909090923 | 5.692306363636362 | 1.5572004545454543 | 4.244688256198348 | 2.1339033057851235 | 2.4626329008264465 | 3.6444684315008318 | 32.402351737495025 | 2.4248732556365695 | 22.420796260039744 | 7.770446710131176 | 7.478487968897912 | 4.735060322745608 | 2.7875520999850703 | 2.7346824256022693 |
| user_004 | Walking | 1.446046380575e12 | -0.420925 | -7.85507 | -0.383802 | -6.384959545454544 | -11.648776363636363 | 1.0514675454545457 | 0.177177855625 | 61.70212470490001 | 0.147303975204 | 7.875697209500185 | 13.651507430224711 | 5.964034545454544 | 3.793706363636363 | 1.4352695454545457 | 4.584503958677686 | 2.8702235537190073 | 2.0461770743801653 | 35.56970805937519 | 14.392207973495037 | 2.0599986681092983 | 25.27148899719583 | 11.122186393466336 | 5.8375284005799495 | 5.027075590957016 | 3.334994211908971 | 2.416097763042702 |
| user_004 | Walking | 1.4460463806e12 | 2.56806 | -7.77842 | -1.72501 | -4.089224636363635 | -9.565543636363635 | 1.0967554545454545 | 6.5949321636 | 60.50381769639999 | 2.9756595001 | 8.37104589403857 | 11.233708301335826 | 6.657284636363635 | 1.7871236363636358 | 2.8217654545454547 | 4.754886892561983 | 3.1147142148760327 | 2.4046774793388432 | 44.3194387295633 | 3.193810891649585 | 7.962360280466116 | 27.304246817124593 | 12.254034792633654 | 7.042212197546262 | 5.2253465738766645 | 3.50057635149323 | 2.6537166762008075 |
| user_004 | Walking | 1.446046380705e12 | -2.2603 | -5.24928 | 0.612526 | 0.15387963636363622 | -7.489280909090908 | -0.35244881818181834 | 5.1089560899999995 | 27.5549405184 | 0.375188100676 | 5.747963527117756 | 7.696832336600777 | 2.4141796363636363 | 2.2400009090909085 | 0.9649748181818183 | 1.7016117685950414 | 1.1223742975206612 | 1.0938697603305785 | 5.828263316632859 | 5.017604072728097 | 0.9311763997250333 | 3.5816168598145435 | 1.6436080382217126 | 1.3978819028483314 | 1.8925160130932956 | 1.2820327757985412 | 1.182320558413974 |
| user_004 | Walking | 1.44604638077e12 | -1.99206 | -2.79678 | -2.14654 | -2.563379090909091 | -3.9324563636363634 | -0.26884118181818184 | 3.9683030435999997 | 7.8219783684 | 4.607633971599999 | 4.049433958419374 | 4.947000242983515 | 0.5713190909090913 | 1.1356763636363634 | 1.877698818181818 | 1.6468231322314053 | 2.134855041322314 | 1.0469995785123967 | 0.3264055036371905 | 1.2897608029223135 | 3.525752851801396 | 3.4760615893678377 | 4.818448231741398 | 1.4022395321709322 | 1.8644199069329415 | 2.1950964060244367 | 1.1841619535227992 |
| user_004 | Walking | 1.446046380859e12 | 3.41111 | -12.87503 | -7.05154 | 0.9411348181818181 | -6.242183636363635 | -5.807813636363637 | 11.635671432099999 | 165.7663975009 | 49.7242163716 | 15.070709515633297 | 8.799682988930867 | 2.4699751818181817 | 6.632846363636365 | 1.2437263636363634 | 2.124358553719008 | 2.597910909090909 | 2.705813090909091 | 6.100777398797759 | 43.99465088360415 | 1.5468552676041316 | 4.8233716870517815 | 11.913549102438015 | 7.848696017596826 | 2.1962175864544435 | 3.451600947739761 | 2.801552429921101 |
| user_004 | Walking | 1.446046380891e12 | 3.18118 | -18.96796 | -9.92556 | 2.306729909090909 | -11.64534818181818 | -7.605384545454546 | 10.1199061924 | 359.78350656160006 | 98.51674131360002 | 21.643016288576785 | 14.348017876777773 | 0.8744500909090909 | 7.322611818181821 | 2.3201754545454545 | 1.9647614958677684 | 5.473180247933884 | 2.4341068181818177 | 0.7646629614909173 | 53.62064383977608 | 5.383214139875206 | 4.369924638387685 | 38.09977452352375 | 6.965246370783482 | 2.0904364707849137 | 6.172501480236659 | 2.639175320205818 |
| user_004 | Walking | 1.446046380907e12 | 0.805325 | -13.90968 | -7.08986 | 2.522717363636364 | -13.554396363636364 | -7.925881818181819 | 0.6485483556249999 | 193.4791977024 | 50.2661148196 | 15.633101447813386 | 16.06838302763538 | 1.717392363636364 | 0.3552836363636356 | 0.8360218181818189 | 1.7950017685950412 | 5.5650118181818184 | 1.963494917355372 | 2.949436530676497 | 0.12622646226776804 | 0.6989324804760342 | 3.9698473877730645 | 38.85068078559317 | 5.016328901239086 | 1.992447587208523 | 6.233031428253284 | 2.2397162546267073 |
| user_004 | Walking | 1.446046381093e12 | -2.87342 | -6.32225 | 2.60518 | -3.528353636363636 | -3.8035636363636356 | 0.5324024545454548 | 8.2565424964 | 39.970845062500004 | 6.7869628323999995 | 7.417165927178655 | 6.15649948995148 | 0.6549336363636362 | 2.5186863636363648 | 2.072777545454545 | 0.7600721487603308 | 5.7483628925619845 | 2.2824336776859506 | 0.42893806804049567 | 6.343780998367774 | 4.296406752940568 | 0.6256039610406465 | 42.857244487174086 | 6.524272663369155 | 0.7909513013078913 | 6.54654446919702 | 2.554265581995959 |
| user_004 | Walking | 1.446046381142e12 | 2.49142 | -19.58108 | -0.268841 | -1.9920570909090907 | -9.736243636363637 | 2.741045363636364 | 6.207173616400001 | 383.4186939664 | 7.2275483281e-2 | 19.74077361873341 | 10.98631488759749 | 4.483477090909091 | 9.844836363636363 | 3.009886363636364 | 1.403917173553719 | 6.001404297520661 | 1.9780878512396696 | 20.10156682470664 | 96.92080302677684 | 9.059415922004135 | 3.3677258738954197 | 47.88211874362117 | 4.570802272349504 | 1.835136472825773 | 6.919690653751884 | 2.137943467996641 |
| user_004 | Walking | 1.446046381279e12 | -1.07237 | -9.00468 | 0.191003 | -2.5633797272727277 | -6.440698181818181 | 1.6994290909090908 | 1.1499774169 | 81.0842619024 | 3.6482146009e-2 | 9.070320913027775 | 7.289577639487164 | 1.4910097272727276 | 2.5639818181818192 | 1.5084260909090907 | 2.744811785123967 | 3.074810578512397 | 1.7380314958677685 | 2.2231100068218934 | 6.574002763966948 | 2.2753492717352803 | 9.677687243315063 | 14.133743354002858 | 3.9411153660728275 | 3.1108981409417864 | 3.7594871131582375 | 1.9852242609017319 |
| user_004 | Walking | 1.446046381312e12 | 0.268841 | -10.15428 | -1.41845 | -1.8283258181818183 | -8.03970090909091 | 0.5846565454545453 | 7.2275483281e-2 | 103.1094023184 | 2.0120004025 | 10.256396940650308 | 8.585679636059387 | 2.0971668181818184 | 2.114579090909089 | 2.0031065454545454 | 2.033827181818182 | 1.8767451239669422 | 1.6579069421487602 | 4.398108663282852 | 4.4714447317099095 | 4.012435832442843 | 5.23885645013378 | 4.130406108873178 | 3.3989535320892843 | 2.288854833783432 | 2.0323400573902926 | 1.8436251061670006 |
| user_004 | Walking | 1.446046381433e12 | 5.21216 | -10.65245 | 1.57053 | 0.6415934545454545 | -9.499355454545453 | 5.8623090909090896e-2 | 27.1666118656 | 113.4746910025 | 2.4665644809 | 11.962770053336309 | 9.817063986868506 | 4.570566545454545 | 1.1530945454545467 | 1.5119069090909092 | 1.6252866528925622 | 0.5504190082644638 | 0.9212699834710744 | 20.890078546428292 | 1.3296270307570277 | 2.2858625017568266 | 4.794645449754335 | 0.436512278266267 | 1.0913816417132172 | 2.189667885720192 | 0.6606907584235359 | 1.0446921277166863 |
| user_004 | Walking | 1.446046381692e12 | 1.68669 | -8.62147 | -1.03525 | -6.06794609090909 | -10.47478090909091 | 0.6787145454545453 | 2.8449231561 | 74.3297449609 | 1.0717425625 | 8.845700123760697 | 12.670474095939682 | 7.75463609090909 | 1.8533109090909097 | 1.7139645454545454 | 5.01806185950413 | 3.1356138842975203 | 1.770334049586777 | 60.134380902429804 | 3.434761325755374 | 2.937674463075206 | 30.033595534096808 | 12.828847254227497 | 3.495879957788058 | 5.480291555574102 | 3.5817380214398007 | 1.869727241548365 |
| user_004 | Walking | 1.446046381725e12 | 3.0279 | -8.73643 | 7.6042e-2 | -1.0270824545454544 | -8.509993636363637 | -0.21658645454545453 | 9.16817841 | 76.3252091449 | 5.782385764e-3 | 9.246576119876156 | 9.62361167764618 | 4.0549824545454545 | 0.22643636363636332 | 0.2926284545454545 | 5.160575347107437 | 2.811317190082644 | 1.6686747933884298 | 16.44288270667148 | 5.1273426776859365e-2 | 8.563141240966113e-2 | 31.85015880880436 | 11.820178771512548 | 3.3603308617290764 | 5.6435944936542315 | 3.4380486866117166 | 1.8331205256962992 |
| user_004 | Walking | 1.446046381741e12 | 1.95493 | -8.08499 | -0.11556 | 0.8401620909090909 | -8.381098181818182 | -0.6381097272727273 | 3.8217513049000003 | 65.36706330009999 | 1.3354113599999998e-2 | 8.318784088951942 | 8.922345281057913 | 1.1147679090909093 | 0.2961081818181821 | 0.5225497272727273 | 5.212196520661156 | 1.9372337190082645 | 1.2978229752066115 | 1.2427074911389178 | 8.768005533966959e-2 | 0.2730582174728017 | 32.01517024982884 | 7.138388465686403 | 2.135986113401629 | 5.658194963928765 | 2.6717762753805574 | 1.4615013217242154 |
| user_004 | Walking | 1.446046381757e12 | 0.958607 | -7.58682 | -0.307161 | 1.7981699999999998 | -8.34626181818182 | -0.7495868181818182 | 0.918927380449 | 57.559837712400004 | 9.434787992100001e-2 | 7.653307322509009 | 8.706527309576039 | 0.8395629999999998 | 0.759441818181819 | 0.44242581818181814 | 4.562017727272726 | 1.136307355371901 | 1.1711444710743801 | 0.7048660309689997 | 0.5767518752033071 | 0.1957406045938512 | 29.026617774266263 | 2.646238876406387 | 1.9367456281675361 | 5.387635638595678 | 1.6267264294915684 | 1.3916700859641757 |
| user_004 | Walking | 1.446046381765e12 | 0.613724 | -6.9737 | 3.7722e-2 | 1.9897712727272732 | -8.241751818181818 | -0.6833971818181819 | 0.37665714817600005 | 48.63249169 | 1.422949284e-3 | 7.000755086950265 | 8.589474710483879 | 1.376047272727273 | 1.2680518181818181 | 0.7211191818181819 | 4.115474834710742 | 0.9076523966942147 | 1.1141392231404958 | 1.893506096780166 | 1.6079554135942147 | 0.5200128743861241 | 25.604283411461083 | 1.4912322128244933 | 1.818784891456646 | 5.06006753032616 | 1.221160191303538 | 1.3486233319413712 |
| user_004 | Walking | 1.446046381773e12 | -0.152682 | -6.51385 | 7.6042e-2 | 1.930549 | -8.081502727272728 | -0.6137239090909091 | 2.3311793124000002e-2 | 42.430241822499994 | 5.782385764e-3 | 6.516082872507685 | 8.42545542549039 | 2.083231 | 1.567652727272728 | 0.6897659090909092 | 3.617310628099173 | 0.7850912396694215 | 1.0476330495867767 | 4.339851399361001 | 2.4575350733256225 | 0.4757770093440084 | 20.798857830615535 | 0.94173193936544 | 1.6783837266054376 | 4.560576480075247 | 0.9704287399729256 | 1.2955244986511978 |
| user_004 | Walking | 1.446046381854e12 | -3.29495 | -2.03038 | -1.41845 | -2.295137181818182 | -3.901106363636364 | 0.38260409090909087 | 10.856695502500001 | 4.1224429444 | 2.0120004025 | 4.12203091320286 | 4.933124619647779 | 0.9998128181818182 | 1.870726363636364 | 1.8010540909090909 | 2.174757355371901 | 2.2833836363636366 | 0.912403132231405 | 0.9996256714006695 | 3.4996171276041337 | 3.2437958383803718 | 4.965430347931929 | 5.354448067415778 | 1.0467234365080729 | 2.2283245607253734 | 2.3139680350894603 | 1.0230950280927344 |
| user_004 | Walking | 1.446046382105e12 | -6.89706 | -17.62674 | 1.57053 | -0.9992135454545454 | -8.41941909090909 | -4.515429454545456 | 47.5694366436 | 310.7019630276001 | 2.4665644809 | 18.993103068011294 | 10.883716871334569 | 5.897846454545454 | 9.207320909090912 | 6.085959454545455 | 2.443632272727273 | 5.591598347107438 | 2.610849041322315 | 34.78459280139438 | 84.7747583229827 | 37.03890248237121 | 9.416257078016788 | 38.75810915089376 | 9.755791198234649 | 3.068592035122425 | 6.225601107595455 | 3.123426195419807 |
| user_004 | Walking | 1.446046382259e12 | 0.652044 | -19.58108 | 4.63616 | -3.1486341818181818 | -7.140913181818183 | 4.005615454545454 | 0.42516137793599995 | 383.4186939664 | 21.493979545600002 | 20.133003623154096 | 9.891322448489866 | 3.800678181818182 | 12.440166818181817 | 0.630544545454546 | 1.3253748760330581 | 7.07025585123967 | 2.7995997024793393 | 14.445154641748761 | 154.7577504641919 | 0.39758642380248 | 2.9688554257230653 | 63.7343600396128 | 11.708537558523775 | 1.7230366872829683 | 7.983380238947209 | 3.4217740367423115 |
| user_004 | Walking | 1.446046382405e12 | -0.497565 | -9.73276 | 0.727487 | -3.3541695454545457 | -6.409347272727273 | 1.9572206363636366 | 0.24757092922499999 | 94.72661721760001 | 0.529237335169 | 9.772585404180104 | 7.8969344004431425 | 2.856604545454546 | 3.3234127272727276 | 1.2297336363636366 | 3.6261769752066115 | 4.021730661157025 | 1.911265520661157 | 8.160189529111573 | 11.04507215579835 | 1.5122448164041329 | 16.706854178363912 | 20.780859285790164 | 5.410361421025659 | 4.087401886084106 | 4.558602777802664 | 2.3260183621428396 |
| user_004 | Walking | 1.446046382429e12 | 1.11189 | -10.07764 | -1.07357 | -2.1836580909090912 | -7.92474 | 1.2604871818181815 | 1.2362993721000002 | 101.55882796960002 | 1.1525525448999998 | 10.195473499872383 | 8.951706201000098 | 3.295548090909091 | 2.1529000000000007 | 2.3340571818181814 | 3.117245702479339 | 2.8157489256198356 | 1.78648673553719 | 10.860637219494555 | 4.634978410000003 | 5.447822927997032 | 12.145998171842267 | 9.098138497830352 | 4.635361589147223 | 3.4851109267629155 | 3.016312069039003 | 2.1529889895555026 |
| user_004 | Walking | 1.446046382623e12 | -1.57053 | -10.69077 | -2.37646 | 1.467222363636364 | -10.732571818181817 | -0.7879069999999999 | 2.4665644809 | 114.2925631929 | 5.647562131599999 | 11.06375568265135 | 11.0931780254364 | 3.037752363636364 | 4.180181818181694e-2 | 1.5885529999999999 | 2.078163867768595 | 0.5615020661157022 | 0.9887275867768593 | 9.227939422778316 | 1.7473920033056814e-3 | 2.523500633809 | 5.248723978201289 | 0.4338176313982718 | 1.2844634011202494 | 2.2910093797715647 | 0.6586483366700866 | 1.1333416965418017 |
| user_004 | Walking | 1.446046382647e12 | -0.152682 | -13.79471 | 0.574206 | 0.3245804545454545 | -11.47110909090909 | -1.1780771818181817 | 2.3311793124000002e-2 | 190.2940239841 | 0.329712530436 | 13.807499712390365 | 11.70239493210674 | 0.47726245454545446 | 2.32360090909091 | 1.7522831818181817 | 1.7605168264462812 | 0.9022678512396695 | 1.1717778842975206 | 0.22777945051875198 | 5.399121184728103 | 3.0704963492828505 | 4.080723327693094 | 1.2877375483407214 | 1.6042709182239578 | 2.020080030021854 | 1.1347852432688406 | 1.266598167622217 |
| user_004 | Walking | 1.446046382777e12 | -9.00468 | -13.75639 | 2.41358 | -3.3646217272727275 | -15.118505454545454 | 0.33731590909090914 | 81.0842619024 | 189.23826583209998 | 5.8253684164 | 16.61769828077583 | 16.21026756275808 | 5.6400582727272734 | 1.3621154545454548 | 2.076264090909091 | 4.350148239669422 | 1.7066785950413221 | 1.3608460661157025 | 31.810257319759355 | 1.855358511511571 | 4.310872575198554 | 24.159749034218965 | 3.67354888521991 | 2.4755485064687686 | 4.915256761779487 | 1.9166504337567427 | 1.5733875893970846 |
| user_004 | Walking | 1.446046382906e12 | 0.498763 | -7.47186 | 0.152682 | 1.3766469090909093 | -8.00138 | -0.9760254545454546 | 0.24876453016900002 | 55.828691859600006 | 2.3311793124000002e-2 | 7.490044604866717 | 8.259255955778732 | 0.8778839090909092 | 0.5295199999999989 | 1.1287074545454547 | 3.590391983471075 | 1.3019398347107431 | 1.4264021900826442 | 0.7706801578407357 | 0.2803914303999988 | 1.2739805179464798 | 19.697873162464898 | 3.4176558799755044 | 3.034293270288825 | 4.4382286063772 | 1.8486903147838214 | 1.7419222916906554 |
| user_004 | Walking | 1.44604638293e12 | -0.842448 | -6.39889 | 1.07237 | 0.9272538181818182 | -7.642561818181818 | -0.6624959090909089 | 0.7097186327039999 | 40.945793232099994 | 1.1499774169 | 6.542590410663348 | 7.862510951834261 | 1.7697018181818183 | 1.2436718181818183 | 1.7348659090909089 | 2.1966091818181823 | 0.696733388429752 | 1.432736685950413 | 3.1318445252760334 | 1.5467195913396696 | 3.0097597225258257 | 8.14653733523913 | 0.8205988078949663 | 2.762373670741838 | 2.854213961012581 | 0.9058690898220152 | 1.6620390099940006 |
| user_004 | Walking | 1.446046382939e12 | -1.45557 | -5.90073 | 0.880768 | 0.5579856363636364 | -7.471862727272727 | -0.36986790909090916 | 2.1186840249000003 | 34.8186145329 | 0.775752269824 | 6.1410952465846025 | 7.645321630071184 | 2.0135556363636367 | 1.571132727272727 | 1.2506359090909092 | 1.808338702479339 | 0.6584127272727271 | 1.2538028925619835 | 4.054406300731769 | 2.468458046707437 | 1.5640901771076448 | 4.9246352946402885 | 0.6840319143592789 | 1.9626212894388848 | 2.219151931400887 | 0.8270622191584373 | 1.4009358620004289 |
| user_004 | Walking | 1.446046382987e12 | -3.02671 | -2.98839 | -0.460442 | -1.6924628181818184 | -5.339860000000001 | 0.5219508181818181 | 9.1609734241 | 8.9304747921 | 0.21200683536400003 | 4.278253738567174 | 5.884753310484754 | 1.3342471818181816 | 2.351470000000001 | 0.9823928181818181 | 1.7649520495867768 | 1.653790413223141 | 1.0248309999999998 | 1.7802155421897596 | 5.529411160900004 | 0.9650956492152147 | 3.3052319820400125 | 3.1935872334277993 | 1.2876016906206604 | 1.8180296977882435 | 1.7870610603523873 | 1.1347253811476417 |
| user_004 | Walking | 1.446046383003e12 | -2.52854 | -2.68182 | -1.34181 | -2.2324302727272727 | -4.510747272727273 | 0.2537086363636363 | 6.3935145316 | 7.192158512400001 | 1.8004540760999999 | 3.922515407248262 | 5.285964041290805 | 0.29610972727272733 | 1.828927272727273 | 1.5955186363636362 | 1.6712091074380169 | 1.894480413223141 | 1.055233900826446 | 8.768097058552896e-2 | 3.3449749689256207 | 2.5456797189836773 | 3.150489087664926 | 3.809061948836891 | 1.3684137055121324 | 1.7749617144222931 | 1.9516818257177297 | 1.1697921633829371 |
| user_004 | Walking | 1.446046383084e12 | 1.22685 | -8.42987 | -8.08618 | -0.953924727272727 | -4.479394545454546 | -4.15661 | 1.5051609225 | 71.06270821689999 | 65.38630699240001 | 11.7453895691799 | 6.405904824638783 | 2.180774727272727 | 3.9504754545454537 | 3.929570000000001 | 1.1439088760330576 | 1.5188780991735535 | 2.5712606528925623 | 4.755778411111437 | 15.606256316966109 | 15.441520384900008 | 1.6986253897774226 | 3.4887752693700222 | 7.339926809683928 | 1.3033132354800294 | 1.867826348826363 | 2.7092299292758315 |
| user_004 | Walking | 1.446046383149e12 | 0.575403 | -13.41151 | -7.24314 | 2.000223181818182 | -13.143269090909088 | -7.5113781818181815 | 0.331088612409 | 179.86860048009999 | 52.4630770596 | 15.253287060568583 | 15.467125608080085 | 1.4248201818181818 | 0.2682409090909115 | 0.26823818181818115 | 1.9575037272727271 | 5.336024214876034 | 2.046493504132231 | 2.030112550516397 | 7.195318530991864e-2 | 7.19517221851236e-2 | 4.43949586617149 | 36.79311558779046 | 5.800020010263088 | 2.1070111215111065 | 6.065732897827802 | 2.4083230701596263 |
| user_005 | Jogging | 1.446046532876e12 | -6.66714 | -15.94065 | 3.67815 | -6.66714 | -15.94065 | 3.67815 | 44.4507557796 | 254.1043224225 | 13.5287874225 | 17.66589555116298 | 17.66589555116298 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_005 | Jogging | 1.446046533395e12 | -0.765807 | 1.72501 | 2.68182 | -0.9659257777777778 | -7.233426000000001 | -1.4993496666666666 | 0.586460361249 | 2.9756595001 | 7.192158512400001 | 3.279371643127537 | 12.016777585242645 | 0.20011877777777776 | 8.958436 | 4.1811696666666665 | 2.509583649250441 | 6.35548094021164 | 2.8759465861111106 | 4.0047525219271594e-2 | 80.25357556609602 | 17.482179781453443 | 13.4248653584389 | 67.15546522672948 | 14.613151611172022 | 3.6639958185618746 | 8.19484381954467 | 3.822715214500293 |
| user_005 | Jogging | 1.44604653443e12 | -4.82776 | -1.41725 | -6.28513 | 0.25142181818181814 | -8.206915818181818 | -0.5963053636363638 | 23.307266617599996 | 2.0085975625 | 39.5028591169 | 8.051007595139877 | 13.313162980793779 | 5.079181818181818 | 6.789665818181818 | 5.688824636363636 | 4.604455165289256 | 7.6510786280991745 | 4.8774477355371895 | 25.798087942148754 | 46.099561922586574 | 32.36272574329785 | 28.396447235708955 | 77.683215025069 | 34.47033206094262 | 5.32883169519445 | 8.813808202194384 | 5.871144016368754 |
| user_005 | Jogging | 1.446046534495e12 | -1.53221 | -0.727487 | -4.17751 | 0.4952790909090908 | -8.241752454545455 | -0.648559909090909 | 2.3476674841 | 0.529237335169 | 17.4515898001 | 4.50871318885655 | 13.218019682091837 | 2.027489090909091 | 7.5142654545454555 | 3.528950090909091 | 4.389100669421488 | 7.629543256198346 | 4.975306644628099 | 4.110712013755372 | 56.46418532137522 | 12.45348874412728 | 27.013036305813625 | 77.35446852814668 | 35.05567044623478 | 5.197406690438379 | 8.795138914658864 | 5.920782925106677 |
| user_005 | Jogging | 1.446046534754e12 | 3.44943 | 11.34341 | 8.81307 | 0.5963047272727273 | -8.231301545454546 | -0.620691181818182 | 11.8985673249 | 128.67295042810002 | 77.6702028249 | 14.77300648405395 | 13.17016081565081 | 2.8531252727272727 | 19.574711545454548 | 9.433761181818182 | 4.122442553719008 | 7.630810066115703 | 5.071264578512397 | 8.140323821875073 | 383.16933208775157 | 88.9958500355796 | 24.749105023319782 | 78.52377984928117 | 36.24373720853375 | 4.974847236179196 | 8.861364446250994 | 6.020277170407834 |
| user_005 | Jogging | 1.446046535078e12 | -0.689167 | -12.83671 | -10.577 | 0.7774559090909091 | -8.325360000000002 | -0.5928216363636364 | 0.47495115388899994 | 164.7811236241 | 111.872929 | 16.64719206887423 | 13.204009114246984 | 1.466622909090909 | 4.511349999999998 | 9.984178363636364 | 4.0093809917355365 | 7.667229809917355 | 5.156455851239669 | 2.150982757470281 | 20.352278822499986 | 99.68381759690449 | 24.96159139444294 | 80.21532640990009 | 35.76035309128915 | 4.996157663089 | 8.956300933415541 | 5.9799960778656995 |
| user_005 | Jogging | 1.446046536955e12 | 1.83997 | 7.20482 | 4.48288 | 1.2616848181818179 | -8.579666363636365 | -1.2268485454545457 | 3.3854896009 | 51.909431232399996 | 20.0962130944 | 8.682806800090624 | 13.573449161694326 | 0.5782851818181822 | 15.784486363636365 | 5.709728545454546 | 4.014131834710743 | 7.9956427355371895 | 5.276803884297521 | 0.3344137515104881 | 249.15000976382234 | 32.60100006277848 | 29.129297786118514 | 81.19264098277382 | 38.57993384112373 | 5.39715645373733 | 9.010695921113632 | 6.211274735601681 |
| user_005 | Jogging | 1.446046537191e12 | -8.88971 | -19.58108 | 8.58315 | -3.7200084545454546 | -12.683423636363635 | 8.053690000000001 | 79.02694388409998 | 383.4186939664 | 73.67046392249999 | 23.154181086209892 | 19.006897586499694 | 5.1697015454545445 | 6.897656363636365 | 0.5294599999999985 | 3.929191355371901 | 8.388347768595041 | 4.496189636363636 | 26.725814069075106 | 47.57766331081324 | 0.2803278915999984 | 37.982441675489866 | 95.07989014595297 | 45.417514090548984 | 6.1629896702404 | 9.75089176157509 | 6.739251745598245 |
| user_005 | Jogging | 1.446046537264e12 | 4.86728 | -6.7821 | -7.1665 | -2.0094745454545455 | -15.167277272727274 | -6.47324909090909 | 23.6904145984 | 45.996880409999996 | 51.35872225 | 11.002091494729536 | 19.015289238467066 | 6.876754545454546 | 8.385177272727274 | 0.69325090909091 | 5.072833107438016 | 4.950287107438016 | 7.389185206611572 | 47.289753078429754 | 70.31119789506201 | 0.4805968229553732 | 29.42502637160394 | 29.293039395937043 | 94.45020276812497 | 5.4244839728405445 | 5.412304444128863 | 9.71854941686901 |
| user_005 | Jogging | 1.446046537353e12 | -4.06135 | 1.72501 | -7.28146 | -7.301162727272728 | -0.2641600909090908 | -5.720777090909092 | 16.4945638225 | 2.9756595001 | 53.0196597316 | 8.514099074723056 | 11.730594574315802 | 3.239812727272728 | 1.9891700909090908 | 1.5606829090909082 | 6.957513388429752 | 5.971951173553719 | 2.344506809917355 | 10.496386507798352 | 3.9567976505672804 | 2.4357311427284603 | 69.01873167429301 | 44.8903587968095 | 10.634663326887337 | 8.30775130070063 | 6.700026775827802 | 3.261083152403099 |
| user_005 | Jogging | 1.446046537393e12 | -0.229323 | -2.56686 | 1.83878 | -5.785767909090908 | -0.12132963636363642 | -2.7979817272727274 | 5.2589038329e-2 | 6.5887702596 | 3.3811118884000004 | 3.1658286729273586 | 8.254692604626396 | 5.556444909090908 | 2.445530363636364 | 4.636761727272727 | 7.184584776859504 | 2.513304768595042 | 3.413041892561983 | 30.874080027762272 | 5.980618759467406 | 21.499559315501166 | 66.18544402612498 | 8.832207393276791 | 15.55851102324376 | 8.135443689567582 | 2.971902991902123 | 3.9444278448519956 |
| user_005 | Jogging | 1.446046537401e12 | -1.64717 | -5.63249 | 2.14534 | -4.468942454545455 | -0.6299449090909092 | -2.268464454545455 | 2.7131690089 | 31.724943600099998 | 4.6024837156 | 6.248247460256356 | 7.318482698991761 | 2.821772454545455 | 5.0025450909090905 | 4.4138044545454544 | 6.129668305785125 | 2.451232454545455 | 3.405758 | 7.96239978523148 | 25.02545738657864 | 19.481669762965296 | 47.990639175892746 | 8.168785599530361 | 15.493628062371334 | 6.927527638046112 | 2.858108745224779 | 3.936194616932874 |
| user_005 | Jogging | 1.446046537433e12 | 2.14654 | -19.38948 | 1.53221 | -0.9121197272727273 | -6.245611909090909 | 6.210718181818149e-2 | 4.607633971599999 | 375.95193467039996 | 2.3476674841 | 19.568015640991806 | 8.791554887799567 | 3.058659727272727 | 13.143868090909091 | 1.4701028181818185 | 4.313094289256198 | 5.166590677685949 | 3.757290925619835 | 9.355399327240072 | 172.7612683912182 | 2.1612022960261252 | 28.42252031728194 | 48.266878089285576 | 18.6166329374183 | 5.331277550201447 | 6.947436799948998 | 4.314699634669637 |
| user_005 | Jogging | 1.446046537442e12 | 7.85626 | -19.58108 | -3.41111 | 0.17129936363636367 | -8.18252918181818 | 0.4139571818181818 | 61.720821187599995 | 383.4186939664 | 11.635671432099999 | 21.372299515637057 | 9.96048220060993 | 7.684960636363636 | 11.39855081818182 | 3.8250671818181816 | 4.717198644628099 | 6.0219889256198345 | 3.9631440413223147 | 59.05861998245858 | 129.92696075467342 | 14.631138945422487 | 32.83726881497833 | 59.71871109874978 | 19.725306374026847 | 5.730381210266759 | 7.727788241065472 | 4.441318089714679 |
| user_005 | Jogging | 1.446046537466e12 | 18.73923 | -19.58108 | -4.36911 | 4.870762727272727 | -13.307 | 8.300990909090897e-2 | 351.1587409929 | 383.4186939664 | 19.0891221921 | 27.45298812791424 | 15.936278926269683 | 13.868467272727273 | 6.27408 | 4.452119909090909 | 6.351353520661157 | 7.969674404958678 | 5.08773411570248 | 192.33438449470742 | 39.364079846399996 | 19.821371684923644 | 68.71057975796339 | 77.23888848308151 | 28.699630476403954 | 8.289184505001888 | 8.788565780779109 | 5.357203606024691 |
| user_005 | Jogging | 1.446046537829e12 | 1.53341 | -7.12698 | 10.92069 | 9.692154545454544 | -13.773812727272729 | 7.349934818181819 | 2.3513462280999997 | 50.79384392039999 | 119.26147007610001 | 13.130371671228502 | 20.121956589371532 | 8.158744545454544 | 6.646832727272729 | 3.570755181818181 | 7.8056263305785105 | 6.036240900826446 | 9.123402008264462 | 66.56511255798428 | 44.18038530434382 | 12.750292568481392 | 80.89015669642863 | 41.34875249110119 | 134.74176434103856 | 8.993895523988959 | 6.4302995646471395 | 11.607832025879706 |
| user_005 | Jogging | 1.446046538129e12 | -1.41725 | -10.1926 | 3.40991 | -1.5008605454545454 | -1.7447167272727275 | -2.132602090909091 | 2.0085975625 | 103.88909476 | 11.6274862081 | 10.840903031140902 | 5.085503723485798 | 8.361054545454549e-2 | 8.447883272727273 | 5.54251209090909 | 4.140810181818183 | 2.3657244958677683 | 2.4502828842975206 | 6.990723311206618e-3 | 71.36673178962526 | 30.719440277873456 | 25.749421180739613 | 10.881888395340889 | 8.121575151195255 | 5.074388749469202 | 3.298770740039521 | 2.849837741204796 |
| user_005 | Jogging | 1.446046538299e12 | 4.63736 | 7.51138 | 1.45557 | 0.7809408181818182 | -10.673349999999997 | -3.0906092727272725 | 21.505107769600002 | 56.4208295044 | 2.1186840249000003 | 8.946765968711823 | 14.588182212283387 | 3.856419181818182 | 18.18473 | 4.546179272727272 | 6.740257842975206 | 6.87802049586777 | 4.3175263719008266 | 14.871968905895217 | 330.6844051728999 | 20.66774597977507 | 62.41186195274679 | 105.32402986327783 | 23.130088554595037 | 7.900117844231616 | 10.262749624894774 | 4.809375069028723 |
| user_005 | Jogging | 1.446046538388e12 | 7.7239e-2 | 1.45677 | 1.37893 | 1.9758369090909091 | 7.068954545454546 | 3.9359427272727263 | 5.965863121e-3 | 2.1221788328999995 | 1.9014479449 | 2.0073845274189495 | 8.44208678770197 | 1.898597909090909 | 5.6121845454545465 | 2.5570127272727263 | 1.3963169338842976 | 7.117761074380166 | 2.578862553719009 | 3.6046740204043717 | 31.496615372238853 | 6.538314087434706 | 3.6102613349908435 | 82.34959291754927 | 9.018596471859075 | 1.9000687711214148 | 9.074667647773623 | 3.0030978125693935 |
| user_005 | Jogging | 1.446046538461e12 | 0.690364 | -14.17792 | -11.30509 | -0.8598659999999998 | -4.3992699090909095 | -4.6791611818181815 | 0.476602452496 | 201.0134155264 | 127.80505990809999 | 18.146489409442147 | 8.478455435481807 | 1.5502299999999998 | 9.77865009090909 | 6.625928818181818 | 1.8035878925619833 | 6.839700892561984 | 5.888343900826445 | 2.403213052899999 | 95.62199760043637 | 43.90293270361231 | 4.423628607856551 | 49.21750137050446 | 51.860472263035305 | 2.103242403494317 | 7.015518610231496 | 7.201421544600434 |
| user_005 | Jogging | 1.446046538567e12 | -1.22565 | -8.73643 | 9.15796 | 8.103603090909091 | -11.944887272727277 | 6.642750909090908 | 1.5022179224999999 | 76.3252091449 | 83.86823136159998 | 12.715960774908044 | 19.144658999574435 | 9.329253090909091 | 3.2084572727272764 | 2.5152090909090914 | 8.351293809917356 | 4.627574297520661 | 8.96885550413223 | 87.03496323423684 | 10.294198070916552 | 6.326276770991738 | 84.04886178978937 | 24.54350544930766 | 101.59656858277818 | 9.167816631553523 | 4.954140233108835 | 10.079512318697676 |
| user_005 | Jogging | 1.446046538607e12 | -7.28026 | -19.58108 | 6.05401 | 0.5266328181818181 | -12.669489090909089 | 8.213884545454546 | 53.0021856676 | 383.4186939664 | 36.6510370801 | 21.75021647510893 | 16.60265102179799 | 7.806892818181819 | 6.911590909090911 | 2.159874545454546 | 6.754191388429752 | 4.713081570247935 | 4.32988041322314 | 60.94757547457886 | 47.77008889462813 | 4.665058052102481 | 51.52626133050314 | 26.113734351315486 | 28.816536944023824 | 7.178179527603301 | 5.110159914456248 | 5.368103663680856 |
| user_005 | Jogging | 1.446046538647e12 | -2.8351 | -17.85667 | -17.39802 | -5.256250272727272 | -17.414240909090907 | 1.0340509999999996 | 8.03779201 | 318.86066348890006 | 302.69109992039995 | 25.091623212125995 | 20.28024650240407 | 2.4211502727272722 | 0.4424290909090942 | 18.432070999999997 | 6.633531214876032 | 4.558850082644629 | 5.790168628099173 | 5.861968643127344 | 0.19574350048264755 | 339.7412413490409 | 52.19864744320685 | 25.50824492503833 | 66.95000848545169 | 7.224863143562434 | 5.050568772429332 | 8.182298484255613 |
| user_005 | Jogging | 1.446046538656e12 | -0.574206 | -15.82569 | -16.24841 | -5.19702809090909 | -18.058719090909094 | -1.2756189999999998 | 0.329712530436 | 250.4524639761 | 264.0108275281 | 22.68905031143075 | 21.186891005724313 | 4.622822090909089 | 2.2330290909090937 | 14.972791 | 6.20567385123967 | 4.47017479338843 | 6.922676074380166 | 21.370484084197084 | 4.986418920846294 | 224.18447032968103 | 46.22914933865778 | 25.025719547759216 | 86.75529880896889 | 6.799202110443384 | 5.002571293620833 | 9.314252455724446 |
| user_005 | Jogging | 1.446046538696e12 | 8.31611 | -6.74378 | -7.08986 | -2.2846848181818173 | -15.250886363636367 | -7.6855626363636365 | 69.1576855321 | 45.4785686884 | 50.2661148196 | 12.84143173637971 | 19.358054965294073 | 10.600794818181818 | 8.507106363636368 | 0.5957026363636366 | 6.402976165289258 | 4.645308099173556 | 7.665329388429751 | 112.37685077719048 | 72.37085868222238 | 0.3548616309705871 | 49.725863424499494 | 26.103591191193857 | 94.9418704171257 | 7.051656785784423 | 5.109167367702281 | 9.743811903825202 |
| user_005 | Jogging | 1.446046538753e12 | -15.44249 | -0.344284 | -2.18486 | -2.319522727272727 | -4.169348636363636 | -7.828393636363637 | 238.4704974001 | 0.11853147265599999 | 4.7736132196000005 | 15.600084682217465 | 14.044445408721396 | 13.122967272727273 | 3.825064636363636 | 5.643533636363637 | 9.416029735537188 | 7.42495634710744 | 1.6822933057851244 | 172.21227004107106 | 14.631119472359673 | 31.849471904767775 | 103.95241120687697 | 62.264699145702394 | 5.839061575357855 | 10.195705527665899 | 7.890798384555418 | 2.416415025478416 |
| user_005 | Jogging | 1.446046538776e12 | -7.31858 | 1.03525 | -6.20849 | -5.831056363636363 | -0.9539253636363635 | -6.699686636363636 | 53.5616132164 | 1.0717425625 | 38.545348080100005 | 9.652911677778887 | 13.13116332092457 | 1.4875236363636368 | 1.9891753636363636 | 0.491196636363636 | 8.796887380165288 | 6.473283157024794 | 2.240312190082645 | 2.2127265687404973 | 3.956818627297859 | 0.24127413557495003 | 98.15646520194038 | 54.52746920713473 | 10.183716786829123 | 9.907394470895987 | 7.384271745211895 | 3.1911936304193644 |
| user_005 | Jogging | 1.446046538842e12 | -1.34061 | -9.96268 | 1.26397 | -3.106829272727273 | -2.922194636363636 | -2.5018703636363635 | 1.7972351721000002 | 99.25499278240001 | 1.5976201609 | 10.131626133814848 | 7.283615235429099 | 1.766219272727273 | 7.040485363636364 | 3.7658403636363635 | 5.634669446280991 | 2.638084099173554 | 3.2736945206611576 | 3.119530519353257 | 49.56843415557786 | 14.181553644392858 | 39.12376106844633 | 10.021174904445111 | 13.79509503835697 | 6.254898965486679 | 3.1656239360424845 | 3.7141748798834135 |
| user_005 | Jogging | 1.446046538866e12 | 2.56806 | -19.46612 | 0.382604 | -0.3930543636363636 | -7.370835181818182 | -0.9760242727272725 | 6.5949321636 | 378.9298278544 | 0.146385820816 | 19.638511803057177 | 8.868827912159022 | 2.9611143636363635 | 12.095284818181817 | 1.3586282727272725 | 4.628840950413223 | 4.8574955537190085 | 3.348752347107438 | 8.768198274533585 | 146.29591483293956 | 1.8458707834538919 | 30.46137151071938 | 38.590619390286015 | 13.792196038869717 | 5.51918214146982 | 6.212134849654023 | 3.7137845978017783 |
| user_005 | Jogging | 1.446046538922e12 | 11.22845 | -19.58108 | -6.70665 | 9.591128545454545 | -17.738221818181817 | -2.181371454545454 | 126.07808940250001 | 383.4186939664 | 44.9791542225 | 23.54731274671061 | 21.66341615767337 | 1.6373214545454555 | 1.8428581818181833 | 4.525278545454546 | 6.907156999999999 | 7.350216272727274 | 3.8332976611570246 | 2.680821545514846 | 3.3961262782942203 | 20.47814591395121 | 76.23218786568253 | 63.583759756504904 | 17.994963219286294 | 8.731104618871688 | 7.973942547855791 | 4.242047055289025 |
| user_005 | Jogging | 1.446046539109e12 | -2.2603 | -0.574206 | -1.34181 | 2.0246095454545454 | 6.494150545454545 | 5.46527 | 5.1089560899999995 | 0.329712530436 | 1.8004540760999999 | 2.690561780843547 | 9.417106115997395 | 4.284909545454545 | 7.068356545454545 | 6.80708 | 3.0412402148760322 | 7.885117867768597 | 3.5615710909090903 | 18.360449812727477 | 49.961664253670115 | 46.3363381264 | 14.181878532462372 | 92.28869650125806 | 17.74296814917574 | 3.7658834995870984 | 9.606700604331232 | 4.212240276761968 |
| user_005 | Jogging | 1.446046539173e12 | 4.06255 | -8.54483 | 1.91542 | -0.19448763636363625 | -6.102783090909091 | -6.051724545454545 | 16.5043125025 | 73.01411972889998 | 3.6688337763999996 | 9.653355168427193 | 10.091842877452285 | 4.257037636363636 | 2.442046909090908 | 7.967144545454545 | 2.8762417603305788 | 7.647278181818183 | 8.382647024793387 | 18.12236943741649 | 5.963593106200457 | 63.475392208166106 | 10.35465508928085 | 63.68957512102852 | 83.8630590189311 | 3.2178649892872837 | 7.980574861564079 | 9.157677599639065 |
| user_005 | Jogging | 1.446046539182e12 | 4.82896 | -4.63616 | 12.56846 | 0.18174836363636357 | -6.64623490909091 | -5.041461818181818 | 23.318854681600005 | 21.493979545600002 | 157.9661867716 | 14.240049894533376 | 11.19579267288441 | 4.647211636363637 | 2.0100749090909096 | 17.609921818181817 | 3.0523252975206607 | 7.180150446280991 | 9.50597 | 21.596575993153593 | 4.0404011401568285 | 310.109346442476 | 11.650190482521412 | 59.41136010555187 | 109.54591898594536 | 3.41323753678548 | 7.7078764978139 | 10.4664186322708 |
| user_005 | Jogging | 1.446046539432e12 | -18.54643 | -1.80046 | -9.04419 | 1.334843636363636 | -7.889902181818181 | -10.256508181818182 | 343.9700657449 | 3.2416562115999996 | 81.7973727561 | 20.712534724475418 | 17.463259594868898 | 19.881273636363638 | 6.089442181818181 | 1.2123181818181816 | 11.065702727272729 | 6.9714458099173555 | 4.298841314049587 | 395.2650414039678 | 37.081306085706565 | 1.4697153739669417 | 142.99720294716028 | 63.301853957224964 | 38.408486209862765 | 11.95814379187507 | 7.956246222762652 | 6.197458044219643 |
| user_005 | Jogging | 1.446046539505e12 | 0.920286 | -2.29862 | 0.191003 | -9.147504363636365 | -2.525057909090909 | -5.947214272727273 | 0.8469263217960001 | 5.2836539044 | 3.6482146009e-2 | 2.483357077064231 | 13.013596496583101 | 10.067790363636366 | 0.226437909090909 | 6.138217272727274 | 10.895003123966942 | 3.147332603305786 | 3.7512741322314045 | 101.36040280612927 | 5.127412667346277e-2 | 37.67771128720745 | 152.4638426241614 | 15.151679892008083 | 18.234761740799325 | 12.34762497908652 | 3.8925158820495622 | 4.270217996870807 |
| user_005 | Jogging | 1.44604653965e12 | -2.14534 | -19.58108 | -9.00587 | 9.608546363636364 | -19.581079999999996 | -4.916048636363636 | 4.6024837156 | 383.4186939664 | 81.1056944569 | 21.659336835159564 | 23.481648866437364 | 11.753886363636365 | 3.552713678800501e-15 | 4.089821363636363 | 8.339577099173555 | 4.43375396694215 | 3.665448603305785 | 138.1538446492769 | 1.2621774483536189e-29 | 16.7266387864564 | 83.59105699229161 | 33.67984891858641 | 16.49654774616765 | 9.142814500595078 | 5.803434234880792 | 4.061594237016747 |
| user_005 | Jogging | 1.446046539716e12 | 4.56072 | 14.40904 | 7.93171 | -1.375448818181818 | -7.924737636363636 | -1.4530909090908766e-2 | 20.8001669184 | 207.62043372159997 | 62.9120235241 | 17.06846871175326 | 17.391907634086433 | 5.936168818181818 | 22.333777636363635 | 7.946240909090909 | 8.200547190082643 | 8.98468811570248 | 6.25096247107438 | 35.23810023795412 | 498.7976235105364 | 63.14274458530991 | 88.97410851906962 | 181.6387913741941 | 46.31359502348609 | 9.432608786495368 | 13.477343631969696 | 6.805409247318348 |
| user_005 | Jogging | 1.446046539747e12 | 0.690364 | 8.92923 | 11.18893 | -0.5393693636363636 | 3.6723814545454547 | 7.140916363636364 | 0.476602452496 | 79.73114839290001 | 125.19215454489998 | 14.331779561181367 | 15.617158981160749 | 1.2297333636363637 | 5.256848545454545 | 4.048013636363635 | 6.121115801652894 | 13.546704859504132 | 7.083557809917355 | 1.512244145640405 | 27.63445662984757 | 16.386414400185938 | 58.020580521352855 | 251.58207109683693 | 54.824987122639506 | 7.617124163445995 | 15.861338880965784 | 7.404389719797271 |
| user_005 | Jogging | 1.446046539885e12 | 4.29247 | -6.05401 | 17.28186 | 0.8366770909090909 | -8.346261727272728 | -5.407247272727274 | 18.425298700899997 | 36.6510370801 | 298.66268505960005 | 18.80795100059015 | 13.44750584742789 | 3.455792909090909 | 2.2922517272727276 | 22.689107272727277 | 1.5008245041322312 | 7.227337421487603 | 11.299107636363637 | 11.942504630523008 | 5.254417981184804 | 514.7955888333258 | 2.962468411960157 | 61.79855321610177 | 154.19796079881098 | 1.7211822715680514 | 7.861205582867158 | 12.41764715229141 |
| user_005 | Jogging | 1.446046540023e12 | -7.66346 | -19.58108 | -3.60271 | -2.960515727272728 | -14.83632909090909 | 5.266703363636363 | 58.728619171599995 | 383.4186939664 | 12.9795193441 | 21.333701799783835 | 17.67996709949153 | 4.702944272727272 | 4.744750909090911 | 8.869413363636363 | 6.033707289256199 | 4.108823966942149 | 4.649743338842975 | 22.117684832378252 | 22.512661189319026 | 78.6664934150513 | 37.483704938843324 | 25.68529101098272 | 33.9780325674715 | 6.1223937262188 | 5.06806580570761 | 5.829067898684275 |
| user_005 | Jogging | 1.446046540039e12 | -0.650847 | -19.58108 | -15.36704 | -3.378555909090909 | -16.710539999999995 | 0.7692924545454531 | 0.42360181740899994 | 383.4186939664 | 236.14591836159997 | 24.899562529197354 | 19.24158714860748 | 2.727708909090909 | 2.8705400000000054 | 16.136332454545453 | 5.15487405785124 | 4.64119132231405 | 6.272813537190083 | 7.440395892733917 | 8.23999989160003 | 260.38122508361687 | 30.22391534719549 | 27.7202941407852 | 67.74329423298182 | 5.497628156504902 | 5.265006566072373 | 8.230631460160382 |
| user_005 | Jogging | 1.446046540137e12 | -19.58108 | -2.4519 | -5.97857 | -0.6334290909090909 | -4.963624090909092 | -8.80382090909091 | 383.4186939664 | 6.011813610000001 | 35.7432992449 | 20.619743131797254 | 16.05909241623178 | 18.94765090909091 | 2.511724090909092 | 2.825250909090909 | 10.737920123966942 | 7.628275264462809 | 3.242024851239669 | 359.0134749727736 | 6.308757908853104 | 7.982042699319007 | 145.38484253940564 | 74.57561519999079 | 14.833590141372897 | 12.057563706628533 | 8.63571741084612 | 3.851440008798384 |
| user_005 | Jogging | 1.446046540145e12 | -17.12858 | -2.8351 | -2.8363 | -2.3613263636363637 | -3.768726818181818 | -7.978191818181819 | 293.3882528164 | 8.03779201 | 8.04459769 | 17.591777696310285 | 15.838120119261502 | 14.767253636363636 | 0.9336268181818177 | 5.1418918181818185 | 11.628155049586777 | 7.521232082644629 | 2.953197190082645 | 218.071779960695 | 0.8716590356283049 | 26.439051469885126 | 162.9597919738336 | 74.24969775898148 | 10.945713369493298 | 12.765570569850516 | 8.616826431986517 | 3.3084306505491843 |
| user_005 | Jogging | 1.446046540153e12 | -12.98999 | -3.79311 | -1.91661 | -3.9289754545454545 | -2.9779350000000004 | -7.319779090909091 | 168.73984020010002 | 14.387683472099999 | 3.6733938920999996 | 13.667513218003487 | 15.62031062188086 | 9.061014545454546 | 0.8151749999999995 | 5.403169090909092 | 11.8254576446281 | 7.077222702479339 | 3.0627749834710745 | 82.10198459293885 | 0.6645102806249993 | 29.194236224955382 | 166.10710388677384 | 71.3572194396066 | 11.99776784122342 | 12.888254493404988 | 8.447320251985632 | 3.463779415786089 |
| user_005 | Jogging | 1.44604654025e12 | -1.83878 | -15.55745 | 2.79678 | -2.1383713636363635 | -7.443994545454546 | -1.1293063636363634 | 3.3811118884000004 | 242.0342505025 | 7.8219783684 | 15.913432714511975 | 9.685510859791448 | 0.2995913636363634 | 8.113455454545452 | 3.9260863636363634 | 4.633590479338844 | 3.3725050578512397 | 5.092800735537191 | 8.975498516549572e-2 | 65.82815941289334 | 15.414154134731403 | 36.68492211564025 | 18.14740033356985 | 28.118351554473097 | 6.056807914705588 | 4.259976564908526 | 5.302674000395753 |
| user_005 | Jogging | 1.446046540339e12 | -2.10702 | -19.58108 | -9.65732 | 9.967363636363636 | -19.52882545454545 | -3.710701727272727 | 4.439533280399999 | 383.4186939664 | 93.2638295824 | 21.934494679139522 | 23.708771637632957 | 12.074383636363637 | 5.225454545454866e-2 | 5.946618272727273 | 9.42869852892562 | 4.468590330578514 | 4.145560669421488 | 145.79074019808596 | 2.730537520661492e-3 | 35.36226888153389 | 110.2410839227691 | 32.07802959815651 | 22.283775548103858 | 10.499575416309419 | 5.6637469574616865 | 4.720569409308993 |
| user_005 | Jogging | 1.446046540363e12 | -8.04667 | -18.39315 | -0.345482 | 6.685750909090908 | -19.47308636363636 | -3.8570151818181815 | 64.7488980889 | 338.30796692249993 | 0.119357812324 | 20.07924856222772 | 23.737894362214355 | 14.732420909090909 | 1.0799363636363601 | 3.5115331818181814 | 11.771304966942148 | 2.058528099173556 | 3.738288214876033 | 217.044225842619 | 1.1662625495041248 | 12.330865287010122 | 156.6251399409877 | 8.933381865150865 | 18.66196161047686 | 12.514996601716986 | 2.98887635494526 | 4.319949260173881 |
| user_005 | Jogging | 1.446046540522e12 | -0.305964 | -7.66346 | -11.42005 | -2.5564122727272727 | -6.144585272727273 | -0.2897438181818185 | 9.361396929600001e-2 | 58.728619171599995 | 130.4175420025 | 13.756444858443478 | 15.856074884135257 | 2.2504482727272728 | 1.5188747272727268 | 11.130306181818181 | 8.48620773553719 | 8.865609454545456 | 7.248238735537189 | 5.064517428221166 | 2.3069804371478 | 123.88371570102001 | 99.27292641162785 | 168.106297538483 | 65.30499509597418 | 9.96357999976052 | 12.965581264967762 | 8.081150604708105 |
| user_005 | Jogging | 1.446046540563e12 | 4.33079 | -10.57581 | 1.14901 | 1.3104566363636363 | -4.935754363636364 | -4.727931818181819 | 18.7557420241 | 111.84775715610002 | 1.3202239801000002 | 11.485805290022116 | 16.556757493226794 | 3.020333363636364 | 5.640055636363637 | 5.87694181818182 | 3.7015521404958673 | 12.720126892561984 | 11.229750066115702 | 9.122413627494954 | 31.81022758127723 | 34.53844513429423 | 17.429640415905563 | 216.0190225727229 | 139.08506675730447 | 4.174882084072023 | 14.697585603517432 | 11.793433204851947 |
| user_005 | Jogging | 1.446046540772e12 | 9.84892 | -3.83143 | -8.16283 | 1.1084052727272726 | -15.58183272727273 | -8.211596636363636 | 97.00122516639999 | 14.6798558449 | 66.6317936089 | 13.353384388244052 | 19.918923756128933 | 8.740514727272727 | 11.750402727272728 | 4.8766636363636096e-2 | 4.9981088264462805 | 5.275534958677687 | 8.059300198347106 | 76.39659769767142 | 138.07196425309837 | 2.3781848222231144e-3 | 32.22934544619266 | 35.61825024324111 | 88.64155595144413 | 5.677089522474756 | 5.968102733971753 | 9.414964468942202 |
| user_005 | Jogging | 1.446046540854e12 | -3.29495 | -2.10702 | -10.80693 | -7.353418181818182 | -1.340610909090909 | -7.194366363636362 | 10.856695502500001 | 4.439533280399999 | 116.7897360249 | 11.492865822230764 | 13.606203557970458 | 4.058468181818182 | 0.7664090909090908 | 3.612563636363637 | 8.992923157024794 | 6.162287355371903 | 2.2906684793388425 | 16.471163982830582 | 0.587382894628099 | 13.050616026776865 | 111.40156660610614 | 74.20178893073263 | 8.095774139881183 | 10.554694055542592 | 8.614046025575474 | 2.845307389348501 |
| user_005 | Jogging | 1.446046541081e12 | -3.44823 | 5.36544 | 7.05034 | 3.9057872727272724 | -14.17094909090909 | -1.7946854545454547 | 11.8902901329 | 28.787946393600006 | 49.70729411560001 | 9.507130515676115 | 18.02446220678441 | 7.354017272727273 | 19.53638909090909 | 8.845025454545455 | 7.398668760330579 | 5.379727933884299 | 4.1227583057851245 | 54.08157004757107 | 381.6704987113917 | 78.23447529155703 | 64.79961115705889 | 68.58351805693644 | 24.705710676729385 | 8.049820566761653 | 8.281516651974833 | 4.970483947940018 |
| user_005 | Jogging | 1.446046541105e12 | 0.690364 | 15.78857 | 11.91702 | 0.9760253636363636 | -5.280636363636363 | 1.9885736363636362 | 0.476602452496 | 249.2789426449 | 142.0153656804 | 19.793203651197953 | 16.32681821965846 | 0.28566136363636363 | 21.06920636363636 | 9.928446363636365 | 5.796183347107438 | 10.402856280991735 | 6.1030641818181826 | 8.160241467458677e-2 | 443.91145679349495 | 98.57404719560415 | 47.5425530104997 | 191.3152041219281 | 46.42550130267332 | 6.895110804802175 | 13.831673945041073 | 6.813626149318241 |
| user_005 | Jogging | 1.446046541161e12 | 3.33447 | 3.52607 | 4.7128 | 1.1502080909090908 | 9.664284545454546 | 10.102029090909092 | 11.1186901809 | 12.4331696449 | 22.21048384 | 6.764787037727056 | 14.376779236094603 | 2.1842619090909094 | 6.138214545454546 | 5.389229090909092 | 3.1340315041322313 | 12.022759669421486 | 6.602495041322314 | 4.771000087505464 | 37.677677806029756 | 29.04379019430084 | 12.984159483889442 | 206.43890998226743 | 57.00039092718214 | 3.60335392154163 | 14.367982112400734 | 7.549860325011459 |
| user_005 | Jogging | 1.446046541202e12 | 0.15388 | -3.21831 | -1.64837 | 2.3137520909090914 | 3.9336547272727276 | 5.702159909090909 | 2.3679054399999996e-2 | 10.357519256099998 | 2.7171236568999997 | 3.619160395367964 | 8.54849712858296 | 2.1598720909090914 | 7.151964727272727 | 7.350529909090909 | 2.2532978925619833 | 6.404875165289256 | 5.647020545454545 | 4.665047449088011 | 51.15059946015325 | 54.03028994444001 | 6.019592997461543 | 48.471210379402834 | 40.376039082987106 | 2.4534858869497382 | 6.962126857462656 | 6.354214277390015 |
| user_005 | Jogging | 1.446046541243e12 | 6.28513 | -19.38948 | -19.12243 | 2.7805632727272727 | -6.196841636363636 | -5.783482818181818 | 39.5028591169 | 375.95193467039996 | 365.66732910490003 | 27.94856208988577 | 11.145794748966885 | 3.504566727272727 | 13.192638363636362 | 13.338947181818185 | 2.0841822975206616 | 9.427113429752067 | 9.935095214876032 | 12.281987945907073 | 174.04570699368992 | 177.9275119193353 | 5.80539289850129 | 97.95673045184817 | 113.73688837266178 | 2.409438295225941 | 9.897309253117646 | 10.664749803566036 |
| user_005 | Jogging | 1.446046541291e12 | 11.38173 | -14.1396 | 1.83878 | 5.1808084545454545 | -11.328278181818183 | -2.508836363636365 | 129.5437777929 | 199.92828816 | 3.3811118884000004 | 18.24426424499766 | 18.29774117897203 | 6.200921545454545 | 2.811321818181817 | 4.347616363636365 | 3.377254479338843 | 6.845085942148759 | 12.621001190082644 | 38.45142801288238 | 7.903530365385117 | 18.90176804535869 | 16.24475305624752 | 73.01666916174067 | 194.4489963055439 | 4.0304780183307685 | 8.544979178543425 | 13.94449699005109 |
| user_005 | Jogging | 1.446046541388e12 | -3.60151 | -19.58108 | -11.07517 | -3.3402345454545452 | -13.547373636363632 | 6.322252727272727 | 12.970874280100002 | 383.4186939664 | 122.6593905289 | 22.782646000309096 | 17.51251075208498 | 0.261275454545455 | 6.033706363636368 | 17.397422727272726 | 5.5190750413223135 | 4.820123553719009 | 5.400312809917355 | 6.82648631479341e-2 | 36.405612482586 | 302.67031755142557 | 35.255405054488655 | 29.324256254471905 | 50.695833226842296 | 5.937626213773368 | 5.415187554874891 | 7.120100647241041 |
| user_005 | Jogging | 1.446046541436e12 | 12.14814 | -11.45717 | -4.25415 | 1.0073781818181815 | -18.337410909090906 | -5.93328090909091 | 147.57730545959998 | 131.2667444089 | 18.0977922225 | 17.23200052492455 | 22.84576654526576 | 11.140761818181819 | 6.880240909090906 | 1.67913090909091 | 7.029086033057851 | 5.398413305785124 | 9.654501570247934 | 124.11657388945787 | 47.33771496712806 | 2.8194806098644656 | 56.89371829485072 | 34.41505704101075 | 156.25215469199318 | 7.542792473272132 | 5.866434781109456 | 12.500086187382596 |
| user_005 | Jogging | 1.446046541542e12 | 0.307161 | -2.68182 | -3.87095 | -7.788874454545455 | -1.243068181818182 | -8.194178181818181 | 9.434787992100001e-2 | 7.192158512400001 | 14.9842539025 | 4.719190639804775 | 14.113925983821156 | 8.096035454545456 | 1.4387518181818182 | 4.32322818181818 | 10.823112314049588 | 4.726066363636364 | 2.2985858677685944 | 65.54579008125705 | 2.0700067943214875 | 18.69030191206693 | 164.5818232479703 | 64.88748743124589 | 7.553243462068892 | 12.828944744131151 | 8.055276992831836 | 2.74831647778579 |
| user_005 | Jogging | 1.446046541696e12 | 5.13552 | -19.58108 | -3.44943 | 7.4800272727272725 | -19.48353727272727 | 0.285061818181818 | 26.373565670399998 | 383.4186939664 | 11.8985673249 | 20.53511205135487 | 23.603902055517196 | 2.344507272727273 | 9.754272727273161e-2 | 3.734491818181818 | 10.172302371900825 | 5.747412975206613 | 4.432490809917356 | 5.496714351871075 | 9.5145836438025e-3 | 13.946429140066941 | 141.17685676629932 | 51.121567963913215 | 29.01355584951671 | 11.881786766572581 | 7.149934822354202 | 5.386423289114652 |
| user_005 | Jogging | 1.44604654172e12 | -7.66346 | -17.70339 | 1.76214 | 8.159341999999999 | -19.410380909090907 | -1.2721369090909092 | 58.728619171599995 | 313.41001749209994 | 3.1051373796000004 | 19.371209927190918 | 23.13952318159967 | 15.822802 | 1.7069909090909086 | 3.0342769090909094 | 12.676106892561982 | 2.8030826446281014 | 3.9175418099173553 | 250.361063131204 | 2.9138179637190063 | 9.206836361042283 | 185.12551732456777 | 15.969343900020288 | 25.30357017118571 | 13.606083834982341 | 3.9961661502020016 | 5.030265417568511 |
| user_005 | Jogging | 1.4460465418e12 | -2.33694 | 10.07884 | 12.72175 | -1.7795533636363636 | 5.564011818181817 | 11.467626363636365 | 5.461288563599999 | 101.5830157456 | 161.8429230625 | 16.397781172210465 | 18.88500083389981 | 0.5573866363636362 | 4.514828181818182 | 1.2541236363636354 | 5.894359570247934 | 14.68871404958678 | 6.774461652892561 | 0.31067986239676837 | 20.383673511339673 | 1.572826095285948 | 63.32538403099489 | 286.51145284843136 | 52.4522313273327 | 7.957724802416511 | 16.926649191391405 | 7.242391271350416 |
| user_005 | Jogging | 1.446046541922e12 | 9.73396 | -12.68342 | -3.37279 | 4.647808181818181 | -9.004675272727273 | -10.085808545454546 | 94.7499772816 | 160.8691428964 | 11.375712384100002 | 16.33997651657125 | 15.640494555029901 | 5.086151818181818 | 3.6787447272727274 | 6.7130185454545455 | 3.0102033719008263 | 10.432625958677686 | 11.195548082644628 | 25.868940317594216 | 13.533162768436894 | 45.064617991616664 | 13.451884134769642 | 124.36908398090496 | 137.54178434293215 | 3.6676810295839033 | 11.152088772104756 | 11.727820954590506 |
| user_005 | Jogging | 1.446046542092e12 | 5.86361 | -18.92964 | -16.32505 | -1.162944727272727 | -17.23309 | -3.9615254545454546 | 34.3819222321 | 358.3312705296 | 266.5072575025 | 25.67528870848778 | 21.256831807594367 | 7.026554727272727 | 1.6965499999999984 | 12.363524545454545 | 3.595142479338843 | 4.947120743801654 | 9.631383471074379 | 49.37247133535871 | 2.8782819024999946 | 152.856739186057 | 16.645394279775534 | 28.83143743943179 | 146.05322223852409 | 4.079876748110847 | 5.369491357608446 | 12.085248124822431 |
| user_005 | Jogging | 1.446046542181e12 | -15.97897 | -5.24928 | -11.26677 | 1.3000036363636367 | -3.256627272727273 | -8.998904545454543 | 255.3274822609 | 27.5549405184 | 126.94010623289998 | 20.244073923304075 | 15.630175991661531 | 17.278973636363638 | 1.9926527272727266 | 2.267865454545456 | 10.113078115702478 | 8.66989082644628 | 3.8348817355371896 | 298.56292992614965 | 3.970664891507435 | 5.143213719920667 | 122.15093013029342 | 114.43345124093472 | 17.24598361886949 | 11.052191191356282 | 10.697357208251706 | 4.152828387842374 |
| user_005 | Jogging | 1.446046542222e12 | 0.422122 | -2.41358 | -6.09353 | -6.357091636363637 | -1.2709399999999997 | -7.211784636363637 | 0.178186982884 | 5.8253684164 | 37.1311078609 | 6.567698475126884 | 12.67392205058049 | 6.779213636363637 | 1.1426400000000003 | 1.1182546363636368 | 9.366308314049588 | 5.309105123966943 | 3.5045659752066105 | 45.95773752745868 | 1.3056261696000007 | 1.2504934317487695 | 118.53790978460839 | 63.76154511363488 | 15.934000527197712 | 10.887511643374182 | 7.985082661665744 | 3.99174154063082 |
| user_005 | Jogging | 1.446046542424e12 | -4.40624 | -11.80206 | 6.09233 | 4.139191909090909 | -18.246837272727273 | -0.3245805454545451 | 19.414950937600004 | 139.2886202436 | 37.11648482889999 | 13.993571953225523 | 19.948414998235947 | 8.545431909090908 | 6.444777272727272 | 6.416910545454544 | 8.179328041322314 | 2.9753652892562 | 2.8794063636363636 | 73.02440651290908 | 41.535154095061976 | 41.17674094836574 | 79.82707085325661 | 14.223698002062362 | 13.350041030829283 | 8.934599647060669 | 3.77143182386509 | 3.6537707961541983 |
| user_005 | Jogging | 1.446046542651e12 | 1.53341 | -16.82202 | 19.08292 | 6.264227999999999 | -17.29928 | -3.9127539090909083 | 2.3513462280999997 | 282.98035688039994 | 364.15783572640004 | 25.48508463464267 | 24.160861800359214 | 4.730817999999999 | 0.47726000000000113 | 22.99567390909091 | 7.284976140495868 | 8.19864711570248 | 14.780556867768594 | 22.380638949123995 | 0.22777710760000108 | 528.8010185332445 | 56.60867013632807 | 109.01950719672978 | 257.7931864713368 | 7.523873346643208 | 10.441240692404795 | 16.055939289600495 |
| user_005 | Jogging | 1.446046542667e12 | -0.420925 | -5.47921 | 9.96268 | 4.801088818181818 | -15.47384090909091 | 1.4207360909090907 | 0.177177855625 | 30.021742224100002 | 99.25499278240001 | 11.377781543962119 | 21.644529400745 | 5.222013818181818 | 9.99463090909091 | 8.54194390909091 | 6.929325727272727 | 6.4564976942148755 | 13.418443363636364 | 27.269428317281847 | 99.8926470089554 | 72.9648057460553 | 51.89172275386088 | 62.058389545976496 | 219.45993950684507 | 7.203590962420123 | 7.877714741343234 | 14.814180352177608 |
| user_005 | Jogging | 1.446046542675e12 | -1.83878 | -4.9044 | 6.39889 | 3.5400015454545466 | -14.139597272727274 | 3.779177 | 3.3811118884000004 | 24.05313936 | 40.945793232099994 | 8.269222725292867 | 19.653619575324555 | 5.3787815454545465 | 9.235197272727273 | 2.619713 | 6.586026388429753 | 5.882959925619835 | 12.331539462809918 | 28.9312909137224 | 85.28886866618926 | 6.862896202369 | 46.90225966063993 | 47.84651938125171 | 200.77022596340439 | 6.848522443610733 | 6.917117852202007 | 14.169341056076123 |
| user_005 | Jogging | 1.446046542739e12 | 1.07357 | -19.58108 | -14.63896 | 0.30367772727272724 | -11.370084545454546 | 3.1137954545454547 | 1.1525525448999998 | 383.4186939664 | 214.29914988160002 | 24.471828627891707 | 14.200580585542609 | 0.7698922727272727 | 8.210995454545454 | 17.752755454545454 | 2.8844750495867775 | 6.841283471074382 | 6.838433719008266 | 0.5927341116051652 | 67.42044635456611 | 315.1603262288934 | 11.334512609664113 | 53.77129461838979 | 75.9497419307865 | 3.3666767901989214 | 7.332891286415597 | 8.714914912423787 |
| user_005 | Jogging | 1.446046542861e12 | -4.09967 | 2.14654 | -2.2615 | -0.7971601818181817 | -5.395598454545454 | -7.67162909090909 | 16.8072941089 | 4.607633971599999 | 5.114382249999999 | 5.15066115469655 | 10.56409483567436 | 3.302509818181818 | 7.542138454545454 | 5.41012909090909 | 1.284205123966942 | 7.828111231404958 | 4.942052809917355 | 10.906571099187305 | 56.883852467533295 | 29.26949678030082 | 2.4156453179541844 | 68.85564282404087 | 28.973992253594517 | 1.5542346405720677 | 8.297930032486468 | 5.382749506859344 |
| user_005 | Jogging | 1.446046542942e12 | 0.307161 | -1.76214 | -4.17751 | -3.141665909090909 | 2.9024918181818182 | -3.5748390909090904 | 9.434787992100001e-2 | 3.1051373796000004 | 17.4515898001 | 4.544345393961709 | 6.196367795879178 | 3.4488269090909087 | 4.664631818181818 | 0.6026709090909095 | 2.230811289256198 | 4.1471462314049585 | 1.8555266115702478 | 11.894407048869551 | 21.758789999194217 | 0.3632122246644633 | 5.7556637291912045 | 21.774195531772843 | 5.234672484273779 | 2.3990964401605877 | 4.666282838810014 | 2.2879406645002356 |
| user_005 | Jogging | 1.446046543055e12 | 9.12083 | -19.58108 | -7.51138 | 5.807868272727273 | -19.180459090909086 | -2.620314909090909 | 83.1895398889 | 383.4186939664 | 56.4208295044 | 22.869828669224873 | 22.320706230307078 | 3.312961727272727 | 0.4006209090909145 | 4.891065090909091 | 7.21340320661157 | 7.456624793388429 | 4.508180297520661 | 10.97571540637389 | 0.1604971128008308 | 23.922517723509557 | 70.92082738462611 | 81.22563714319311 | 25.297300130523492 | 8.421450432355824 | 9.012526679194526 | 5.029642147362324 |
| user_005 | Jogging | 1.44604654333e12 | 3.44943 | -19.58108 | 16.4005 | 2.7352753636363634 | -13.958446363636364 | -6.299065454545453 | 11.8985673249 | 383.4186939664 | 268.97640025000004 | 25.773894962564352 | 21.0075486111602 | 0.7141546363636366 | 5.622633636363636 | 22.699565454545453 | 3.6011598842975205 | 9.98418388429752 | 12.89937814876033 | 0.5100168446396781 | 31.614009008767763 | 515.2702718251934 | 18.185449279429097 | 109.58084417423078 | 215.4924638985187 | 4.264440089792457 | 10.468086939562108 | 14.679661573024042 |
| user_005 | Jogging | 1.446046543339e12 | 4.98224 | -19.58108 | 0.919089 | 2.4635499090909083 | -15.49125909090909 | -4.957854636363636 | 24.8227154176 | 383.4186939664 | 0.8447245899210001 | 20.225877829501517 | 21.37385900560983 | 2.5186900909090917 | 4.089820909090911 | 5.876943636363636 | 3.4383779917355373 | 9.535741305785121 | 12.211196347107439 | 6.3437997740436485 | 16.7266350684372 | 34.53846650499504 | 17.073977609871836 | 103.70063629752617 | 202.1941164653598 | 4.132066990002925 | 10.183350936579087 | 14.219497757141768 |
| user_005 | Jogging | 1.446046543371e12 | -2.10702 | -19.58108 | 3.17999 | -0.5742053636363637 | -17.881052727272728 | 2.1383716363636363 | 4.439533280399999 | 383.4186939664 | 10.1123364001 | 19.949199574090684 | 19.746132967476807 | 1.5328146363636361 | 1.7000272727272723 | 1.0416183636363638 | 3.6401132148760342 | 5.236264876033058 | 7.928505719008263 | 2.349520709450586 | 2.8900927280165276 | 1.0849688154644963 | 17.808548436835352 | 36.04540436756053 | 138.59882739066543 | 4.220017587266119 | 6.003782505018027 | 11.772800320682647 |
| user_005 | Jogging | 1.446046543403e12 | 1.45677 | -19.35116 | -12.2631 | -6.367545454545442e-3 | -19.312838181818176 | 0.7867098181818175 | 2.1221788328999995 | 374.4673933456 | 150.38362161 | 22.955896710616642 | 21.424840470675225 | 1.4631375454545454 | 3.832181818182434e-2 | 13.049809818181817 | 1.719979917355372 | 2.37902555371901 | 8.625239404958677 | 2.140771476918752 | 1.4685617487608027e-3 | 170.29753629071456 | 4.406007892590968 | 10.773255456819701 | 151.92967159914915 | 2.0990492830305265 | 3.2822637701470154 | 12.325975482660557 |
| user_005 | Jogging | 1.446046543436e12 | 8.62267 | -9.57948 | -10.04052 | 1.533412818181818 | -17.522235454545452 | -6.330417363636363 | 74.35043792889999 | 91.7664370704 | 100.81204187040001 | 16.337959385116 | 20.2231033082854 | 7.089257181818182 | 7.942755454545452 | 3.7101026363636374 | 2.958264413223141 | 2.078480743801654 | 5.803152570247933 | 50.25756738996067 | 63.08736421071153 | 13.764861572352412 | 14.241782773955505 | 9.142709415267989 | 55.880129580603835 | 3.7738286625064874 | 3.0236913558212235 | 7.475301303666885 |
| user_005 | Jogging | 1.446046543444e12 | 8.69931 | -5.59417 | -9.84892 | 2.5645773636363636 | -16.39701181818182 | -7.197850000000002 | 75.67799447610001 | 31.2947379889 | 97.00122516639999 | 14.281945162736061 | 19.86986137091605 | 6.134732636363637 | 10.80284181818182 | 2.651069999999998 | 3.1872362975206614 | 2.9557304958677695 | 5.917480165289256 | 37.63494451966514 | 116.70139134865789 | 7.028172144899989 | 16.474435994988536 | 19.63105162598625 | 56.34253170650897 | 4.058871271054126 | 4.430694260043932 | 7.506166245594949 |
| user_005 | Jogging | 1.446046543508e12 | -0.919089 | 2.56806 | -5.0972 | 4.104354909090909 | -2.929164636363637 | -8.281270909090908 | 0.8447245899210001 | 6.5949321636 | 25.98144784 | 5.781098908816645 | 10.802467948038467 | 5.0234439090909095 | 5.497224636363637 | 3.1840709090909085 | 4.073036859504132 | 8.464671801652893 | 2.309668578512397 | 25.23498870778256 | 30.219478702643315 | 10.138307554119004 | 20.73373618005687 | 76.00364634040206 | 6.9007059023571315 | 4.553431253467748 | 8.718007016537785 | 2.6269194700936556 |
| user_005 | Jogging | 1.446046543541e12 | -3.83143 | 1.18853 | -0.881966 | 0.17478127272727265 | 0.7983571818181816 | -5.821804181818181 | 14.6798558449 | 1.4126035609000003 | 0.7778640251560001 | 4.107349927989579 | 6.9965167402069 | 4.006211272727273 | 0.3901728181818185 | 4.939838181818181 | 3.644546297520661 | 6.136317785123967 | 2.6650025619834707 | 16.049728761727078 | 0.1522348280479424 | 24.40200126254875 | 15.574869901198497 | 49.55435757769464 | 8.901935284719384 | 3.946500969364951 | 7.039485604623014 | 2.983611114860545 |
| user_005 | Jogging | 1.446046543581e12 | -1.83878 | -1.11069 | -0.537083 | -2.3996464545454548 | 0.9272535454545455 | -2.376459090909091 | 3.3811118884000004 | 1.2336322760999998 | 0.28845814888899995 | 2.2143175728402196 | 4.207773037473346 | 0.5608664545454547 | 2.0379435454545454 | 1.839376090909091 | 3.172033743801653 | 2.5851962644628106 | 3.4196922975206614 | 0.3145711798343886 | 4.153213894459842 | 3.3833044038080087 | 13.604792506818278 | 10.39215341495098 | 12.482257721226553 | 3.6884675011199812 | 3.2236863083977294 | 3.5330238778172096 |
| user_005 | Jogging | 1.446046543768e12 | -2.10702 | -1.22565 | 7.39522 | 0.8436457272727271 | -7.370835454545454 | 0.8041268181818185 | 4.439533280399999 | 1.5022179224999999 | 54.6892788484 | 7.786592968127973 | 9.637813049839506 | 2.9506657272727272 | 6.145185454545454 | 6.591093181818182 | 2.724542628099174 | 7.275158842975207 | 4.76882152892562 | 8.706428234101892 | 37.76330427075702 | 43.44250933141012 | 9.235165670215759 | 61.3999239324024 | 28.268622903381537 | 3.038941537808149 | 7.835810355821687 | 5.316824513126377 |
| user_005 | Jogging | 1.446046543776e12 | -1.14901 | -1.8771 | 6.39889 | 0.8297110909090908 | -5.761382727272728 | 1.9537359090909092 | 1.3202239801000002 | 3.52350441 | 40.945793232099994 | 6.766795520938992 | 8.382296009008487 | 1.9787210909090909 | 3.8842827272727276 | 4.4451540909090905 | 2.794532537190083 | 7.244439008264464 | 4.54301652892562 | 3.9153371556084626 | 15.087652305389259 | 19.759394891925822 | 9.458262227633652 | 61.15089409500795 | 25.700279986111074 | 3.075428787605665 | 7.819903713921799 | 5.069544356854083 |
| user_005 | Jogging | 1.446046543899e12 | 8.08618 | -19.58108 | -0.345482 | 8.922263636363637 | -3.0336731818181817 | -8.249916545454546 | 65.38630699240001 | 383.4186939664 | 0.119357812324 | 21.18783516008948 | 14.782951570206615 | 0.8360836363636359 | 16.54740681818182 | 7.904434545454547 | 4.691229611570248 | 4.17659847107438 | 7.8366609090909085 | 0.6990358469950406 | 273.8166724064102 | 62.48008548337523 | 27.402560312610817 | 41.6009787463552 | 78.44553029380619 | 5.234745486899131 | 6.4498820722828105 | 8.856948136565224 |
| user_005 | Jogging | 1.446046543931e12 | -6.51385 | -19.58108 | -5.32712 | 4.299441818181818 | -9.520255818181818 | -5.097199272727273 | 42.430241822499994 | 383.4186939664 | 28.378207494399998 | 21.312605267383432 | 19.3627663237903 | 10.813291818181817 | 10.060824181818182 | 0.22992072727272728 | 7.600088487603307 | 8.318992033057851 | 8.151140826446282 | 116.92727994515782 | 101.22018321745749 | 5.286354082961984e-2 | 73.86016063086305 | 98.25547117025539 | 110.92824713448081 | 8.594193425264702 | 9.912389780989011 | 10.532247962067776 |
| user_005 | Jogging | 1.44604654398e12 | 9.46572 | -16.89866 | -4.25415 | -0.7205188181818183 | -17.67899909090909 | 1.4381556363636365 | 89.59985511839999 | 285.5647097956 | 18.0977922225 | 19.83084358106079 | 19.670062757952863 | 10.186238818181817 | 0.7803390909090915 | 5.692305636363637 | 7.83032711570248 | 8.036180578512397 | 6.404241578512397 | 103.75946126103409 | 0.6089290968008273 | 32.40234345777723 | 81.04664685578355 | 93.61057053591657 | 85.36081290888455 | 9.002591118993662 | 9.675255579875737 | 9.239091562966813 |
| user_005 | Jogging | 1.446046544255e12 | -5.17264 | 0.498763 | 3.63983 | -4.434106363636364 | -1.9572211818181822 | -0.43257227272727294 | 26.756204569600005 | 0.24876453016900002 | 13.248362428899998 | 6.344551326033151 | 6.585032182207585 | 0.7385336363636368 | 2.4559841818181822 | 4.072402272727273 | 1.7532333884297522 | 4.769137553719007 | 3.826648140495868 | 0.5454319320404964 | 6.031858301341126 | 16.58446027091426 | 3.4661914932591844 | 24.218586942638918 | 19.63088592880002 | 1.8617710636002442 | 4.921238354585045 | 4.430675561220887 |
| user_005 | Jogging | 1.446046544352e12 | -5.70913 | -19.58108 | -6.09353 | -1.7203315454545456 | -11.558200000000001 | -3.216022363636364 | 32.5941653569 | 383.4186939664 | 37.1311078609 | 21.287178469308703 | 12.672935690213166 | 3.9887984545454547 | 8.022879999999999 | 2.8775076363636365 | 2.8122678677685955 | 6.869153115702479 | 2.6387184628099174 | 15.910513110984208 | 64.36660349439998 | 8.280050197331041 | 9.862269849087586 | 58.872521967012815 | 8.851188233063542 | 3.14042510642868 | 7.6728431475570265 | 2.9750946595131293 |
| user_005 | Jogging | 1.446046544392e12 | 3.60271 | -19.58108 | -6.47673 | -1.1733964545454543 | -18.02736545454545 | -6.616078181818182 | 12.9795193441 | 383.4186939664 | 41.9480314929 | 20.936720010627262 | 19.676679123590134 | 4.776106454545454 | 1.5537145454545502 | 0.13934818181818187 | 2.5677780578512395 | 6.881187867768597 | 3.4491445454545455 | 22.811192865150748 | 2.4140288887570396 | 1.9417915776033072e-2 | 8.397670316512064 | 57.70691056056415 | 18.3672542259302 | 2.897873412782564 | 7.596506470777482 | 4.285703469202017 |
| user_005 | Jogging | 1.446046544457e12 | 2.72134 | -2.49022 | 2.4519 | 4.543298181818181 | -13.223392727272726 | -3.0174525454545456 | 7.405691395600001 | 6.2011956484 | 6.011813610000001 | 4.429300244282386 | 15.021875104562008 | 1.821958181818181 | 10.733172727272727 | 5.469352545454546 | 3.766792727272727 | 5.418364876033059 | 4.356480776859505 | 3.3195316162942117 | 115.20099679347108 | 29.913817266470122 | 17.912531199467377 | 44.40494266927388 | 24.473405190686368 | 4.23231983662239 | 6.663703374946538 | 4.947060257434345 |
| user_005 | Jogging | 1.446046544611e12 | -0.689167 | -1.07237 | -2.29982 | 1.8434560909090907 | -0.21538854545454544 | -0.4499924545454545 | 0.47495115388899994 | 1.1499774169 | 5.2891720324 | 2.629467741423918 | 2.762459505633797 | 2.5326230909090905 | 0.8569814545454546 | 1.8498275454545454 | 1.3595797438016528 | 1.0650511983471072 | 2.122819520661157 | 6.414179720605915 | 0.7344172134348431 | 3.4218619479223884 | 2.1293145833974765 | 1.1827212289184335 | 4.709658674995932 | 1.4592171131800356 | 1.0875298749544462 | 2.170174802866334 |
| user_005 | Jogging | 1.446046544821e12 | 5.40376 | -16.93698 | -9.12083 | 0.4116704545454546 | -18.992341818181817 | -3.8605 | 29.2006221376 | 286.86129152039996 | 83.1895398889 | 19.981277575442967 | 20.556470047062273 | 4.9920895454545455 | 2.0553618181818187 | 5.26033 | 4.955671487603306 | 0.8905509917355384 | 3.986896099173553 | 24.92095802983657 | 4.224512203639672 | 27.671071708899998 | 29.33488107108366 | 1.1913207005563502 | 20.324670741318293 | 5.416168486216401 | 1.0914763857071532 | 4.508289114655169 |
- Prediction using Random Forests
Instead of just stopping at ETL and feature engineering let's quickly see without understanding the random forest model in detail how simple it is to predict the activity using random forest model.
val splits = dataFeatDF.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
splits: Array[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = Array([user_id: string, activity: string ... 27 more fields], [user_id: string, activity: string ... 27 more fields])
trainingData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
testData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
See
import org.apache.spark.ml.feature.{StringIndexer,VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
val transformers = Array(
new StringIndexer().setInputCol("activity").setOutputCol("label"),
new VectorAssembler()
.setInputCols(Array("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ"))
.setOutputCol("features")
)
// Train a RandomForest model.
val rf = new RandomForestClassifier()
.setLabelCol("label")
.setFeaturesCol("features")
.setNumTrees(10)
.setFeatureSubsetStrategy("auto")
.setImpurity("gini")
.setMaxDepth(20)
.setMaxBins(32)
.setSeed(12345)
val model = new Pipeline().setStages(transformers :+ rf).fit(trainingData)
import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
transformers: Array[org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable}}] = Array(strIdx_d760fa44cad2, VectorAssembler: uid=vecAssembler_3d7b1fc4476e, handleInvalid=error, numInputCols=6)
rf: org.apache.spark.ml.classification.RandomForestClassifier = rfc_794232dabcdf
model: org.apache.spark.ml.PipelineModel = pipeline_76d41a70ccc7
Before we go further let's find quickly which label is mapped to which activity by the StringIndexer and quickly check that the estimator we have built can transform the union of the training and test data. This is mostly for debugging... not something you would do in a real pipeline except during debugging stages.
val activityLabelDF = model.transform(trainingData.union(testData)).select("activity","label").distinct
display(activityLabelDF) // this just shows what activity is associated with what label
| activity | label |
|---|---|
| Jogging | 0.0 |
| Jumping | 4.0 |
| Standing | 1.0 |
| Walking | 2.0 |
| Sitting | 3.0 |
val myPredictionsOnTestData = model.transform(testData)
display(myPredictionsOnTestData)
Let's evaluate accuracy at a lower level...
val accuracy: Double = 1.0 * model.transform(testData)
.select("activity","label","prediction")
.filter($"label"===$"prediction").count() / testData.count()
accuracy: Double = 0.9850159665929747
We get 98% correct predictions and here are the mis-predicted ones.
display(model.transform(testData).select("activity","label","prediction").filter(not($"label"===$"prediction")))
| activity | label | prediction |
|---|---|---|
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Jogging | 0.0 | 2.0 |
| Jumping | 4.0 | 0.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Jumping | 4.0 | 2.0 |
| Jumping | 4.0 | 2.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 0.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Standing | 1.0 | 2.0 |
| Jogging | 0.0 | 2.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
testData.printSchema()
root
|-- user_id: string (nullable = true)
|-- activity: string (nullable = true)
|-- timeStampAsLong: long (nullable = true)
|-- x: double (nullable = true)
|-- y: double (nullable = true)
|-- z: double (nullable = true)
|-- meanX: double (nullable = true)
|-- meanY: double (nullable = true)
|-- meanZ: double (nullable = true)
|-- SqX: double (nullable = true)
|-- SqY: double (nullable = true)
|-- SqZ: double (nullable = true)
|-- resultant: double (nullable = true)
|-- meanResultant: double (nullable = true)
|-- absDevFromMeanX: double (nullable = true)
|-- absDevFromMeanY: double (nullable = true)
|-- absDevFromMeanZ: double (nullable = true)
|-- meanAbsDevFromMeanX: double (nullable = true)
|-- meanAbsDevFromMeanY: double (nullable = true)
|-- meanAbsDevFromMeanZ: double (nullable = true)
|-- sqrDevFromMeanX: double (nullable = true)
|-- sqrDevFromMeanY: double (nullable = true)
|-- sqrDevFromMeanZ: double (nullable = true)
|-- varianceX: double (nullable = true)
|-- varianceY: double (nullable = true)
|-- varianceZ: double (nullable = true)
|-- stddevX: double (nullable = true)
|-- stddevY: double (nullable = true)
|-- stddevZ: double (nullable = true)
Let's understand the mis-predicitons better by seeing the user_id also.
display(model.transform(testData).select("user_id","activity","label","prediction").filter(not($"label"===$"prediction")))
| user_id | activity | label | prediction |
|---|---|---|---|
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Walking | 2.0 | 0.0 |
| user_002 | Jogging | 0.0 | 2.0 |
| user_002 | Jumping | 4.0 | 0.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Walking | 2.0 | 1.0 |
| user_002 | Walking | 2.0 | 1.0 |
| user_003 | Jumping | 4.0 | 2.0 |
| user_003 | Jumping | 4.0 | 2.0 |
| user_003 | Jumping | 4.0 | 0.0 |
| user_003 | Jumping | 4.0 | 0.0 |
| user_003 | Walking | 2.0 | 1.0 |
| user_003 | Walking | 2.0 | 1.0 |
| user_003 | Walking | 2.0 | 1.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 1.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Standing | 1.0 | 2.0 |
| user_006 | Jogging | 0.0 | 2.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_007 | Walking | 2.0 | 0.0 |
Let's evaluate our model's accuracy using ML pipeline's evaluation.MulticlassClassificationEvaluator:
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// Select (prediction, true label) and compute test error.
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(myPredictionsOnTestData)
println(s"Test Error = ${(1.0 - accuracy)}")
Test Error = 0.014984033407025255
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_656aec85ac9d, metricName=accuracy, metricLabel=0.0, beta=1.0, eps=1.0E-15
accuracy: Double = 0.9850159665929747
Note, it's the same 98% we got from our slightly lower-level operations earlier for accuracy.
Think!
What else do you need to investigate to try to understand the mis-predicitons better?
- time-line?
- are mis-predictions happening during transitions between users or activities of the same user or both, as our Markov process simply uses the same sliding window over the time-odered, user-grouped, activity streams...
- ...
Typically, in inustry you will have to improve on an existing algorithm that is in production and getting into the details under the given constraints needs some thinking from scratch over the entire data science pipeline and process.
Explaining the Model's Predictions to a Curious Client whose Activity is being Predicted
Now, let's try to explain the decision branches in each tree of our best-fitted random forest model. Often, being able to "explain" why the model "predicts" is critical for various businesses. how it predicts.
import org.apache.spark.ml.classification.RandomForestClassificationModel
val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println(s"Learned classification forest model:\n ${rfModel.toDebugString}")
Learned classification forest model:
RandomForestClassificationModel: uid=rfc_794232dabcdf, numTrees=10, numClasses=5, numFeatures=6
Tree 0 (weight 1.0):
If (feature 3 <= 0.5225058552059478)
If (feature 1 <= -8.168595136363637)
If (feature 1 <= -10.122931363636361)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 1 > -10.122931363636361)
If (feature 5 <= 0.80494691075511)
If (feature 4 <= 0.9240406048221913)
If (feature 5 <= 0.4040864257583746)
If (feature 5 <= 0.029334843391680826)
If (feature 1 <= -9.513290454545455)
Predict: 2.0
Else (feature 1 > -9.513290454545455)
Predict: 1.0
Else (feature 5 > 0.029334843391680826)
Predict: 1.0
Else (feature 5 > 0.4040864257583746)
If (feature 2 <= -1.21523575)
Predict: 2.0
Else (feature 2 > -1.21523575)
Predict: 1.0
Else (feature 4 > 0.9240406048221913)
If (feature 1 <= -9.419232727272725)
Predict: 1.0
Else (feature 1 > -9.419232727272725)
Predict: 2.0
Else (feature 5 > 0.80494691075511)
Predict: 2.0
Else (feature 1 > -8.168595136363637)
If (feature 1 <= 3.231696363636363)
If (feature 0 <= -2.5738281818181816)
If (feature 3 <= 0.02774026246959616)
Predict: 4.0
Else (feature 3 > 0.02774026246959616)
Predict: 0.0
Else (feature 0 > -2.5738281818181816)
If (feature 1 <= -4.157154727272728)
If (feature 4 <= 0.02938177926107125)
Predict: 4.0
Else (feature 4 > 0.02938177926107125)
Predict: 2.0
Else (feature 1 > -4.157154727272728)
Predict: 3.0
Else (feature 1 > 3.231696363636363)
If (feature 3 <= 0.07730407812363697)
If (feature 0 <= -3.7931120454545457)
Predict: 0.0
Else (feature 0 > -3.7931120454545457)
Predict: 3.0
Else (feature 3 > 0.07730407812363697)
If (feature 0 <= 0.32806340909090914)
If (feature 4 <= 0.9240406048221913)
Predict: 2.0
Else (feature 4 > 0.9240406048221913)
Predict: 3.0
Else (feature 0 > 0.32806340909090914)
Predict: 4.0
Else (feature 3 > 0.5225058552059478)
If (feature 4 <= 3.509467511028114)
If (feature 5 <= 2.5963437348657123)
If (feature 3 <= 3.2533446617122705)
If (feature 5 <= 0.80494691075511)
If (feature 2 <= 2.8699414545454545)
If (feature 0 <= 2.8798476363636363)
If (feature 0 <= -5.7328163636363625)
If (feature 0 <= -8.90539)
If (feature 3 <= 2.499281243538517)
Predict: 4.0
Else (feature 3 > 2.499281243538517)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 0.0
Else (feature 0 > -5.7328163636363625)
If (feature 3 <= 1.6040286540977868)
If (feature 1 <= -8.617986363636366)
If (feature 4 <= 1.9631927695493916)
If (feature 0 <= -2.5738281818181816)
If (feature 2 <= -0.38031795454545453)
Predict: 1.0
Else (feature 2 > -0.38031795454545453)
Predict: 2.0
Else (feature 0 > -2.5738281818181816)
Predict: 1.0
Else (feature 4 > 1.9631927695493916)
Predict: 0.0
Else (feature 1 > -8.617986363636366)
If (feature 2 <= -1.073567681818182)
Predict: 1.0
Else (feature 2 > -1.073567681818182)
If (feature 3 <= 1.1186607583073207)
Predict: 2.0
Else (feature 3 > 1.1186607583073207)
Predict: 1.0
Else (feature 3 > 1.6040286540977868)
If (feature 2 <= -0.5214064090909091)
If (feature 3 <= 1.8104608428061633)
Predict: 2.0
Else (feature 3 > 1.8104608428061633)
Predict: 4.0
Else (feature 2 > -0.5214064090909091)
Predict: 2.0
Else (feature 0 > 2.8798476363636363)
If (feature 4 <= 0.9240406048221913)
Predict: 1.0
Else (feature 4 > 0.9240406048221913)
Predict: 2.0
Else (feature 2 > 2.8699414545454545)
Predict: 2.0
Else (feature 5 > 0.80494691075511)
If (feature 1 <= -13.949734999999999)
If (feature 2 <= -0.04588640909090911)
If (feature 5 <= 1.468512909467716)
Predict: 0.0
Else (feature 5 > 1.468512909467716)
If (feature 0 <= -0.5881405909090909)
Predict: 2.0
Else (feature 0 > -0.5881405909090909)
Predict: 0.0
Else (feature 2 > -0.04588640909090911)
Predict: 0.0
Else (feature 1 > -13.949734999999999)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -2.4344832727272725)
If (feature 0 <= -0.5881405909090909)
If (feature 1 <= -4.157154727272728)
If (feature 4 <= 0.9240406048221913)
If (feature 3 <= 2.29340842981981)
If (feature 2 <= -0.6868815000000001)
If (feature 0 <= -4.420172227272728)
Predict: 1.0
Else (feature 0 > -4.420172227272728)
Predict: 2.0
Else (feature 2 > -0.6868815000000001)
Predict: 2.0
Else (feature 3 > 2.29340842981981)
If (feature 2 <= -0.1991675454545455)
Predict: 2.0
Else (feature 2 > -0.1991675454545455)
Predict: 1.0
Else (feature 4 > 0.9240406048221913)
Predict: 2.0
Else (feature 1 > -4.157154727272728)
If (feature 4 <= 2.258741828976616)
Predict: 2.0
Else (feature 4 > 2.258741828976616)
Predict: 0.0
Else (feature 0 > -0.5881405909090909)
If (feature 4 <= 0.3869983835216714)
Predict: 1.0
Else (feature 4 > 0.3869983835216714)
If (feature 4 <= 1.3210859432345212)
If (feature 3 <= 1.1186607583073207)
If (feature 4 <= 0.9240406048221913)
If (feature 0 <= -0.16835886363636363)
Predict: 2.0
Else (feature 0 > -0.16835886363636363)
Predict: 1.0
Else (feature 4 > 0.9240406048221913)
Predict: 1.0
Else (feature 3 > 1.1186607583073207)
If (feature 5 <= 1.076775279931446)
If (feature 1 <= -9.2781435)
Predict: 2.0
Else (feature 1 > -9.2781435)
Predict: 1.0
Else (feature 5 > 1.076775279931446)
If (feature 2 <= 0.6160089999999999)
Predict: 2.0
Else (feature 2 > 0.6160089999999999)
Predict: 1.0
Else (feature 4 > 1.3210859432345212)
Predict: 2.0
Else (feature 1 > -2.4344832727272725)
If (feature 1 <= 3.231696363636363)
If (feature 1 <= 1.3574864545454546)
Predict: 0.0
Else (feature 1 > 1.3574864545454546)
If (feature 4 <= 2.258741828976616)
Predict: 4.0
Else (feature 4 > 2.258741828976616)
Predict: 0.0
Else (feature 1 > 3.231696363636363)
If (feature 4 <= 1.6647103963937675)
Predict: 2.0
Else (feature 4 > 1.6647103963937675)
If (feature 2 <= 0.6160089999999999)
Predict: 0.0
Else (feature 2 > 0.6160089999999999)
Predict: 2.0
Else (feature 3 > 3.2533446617122705)
If (feature 4 <= 0.3869983835216714)
Predict: 1.0
Else (feature 4 > 0.3869983835216714)
If (feature 1 <= -6.928410727272727)
If (feature 2 <= -1.21523575)
If (feature 0 <= -7.16007390909091)
If (feature 1 <= -7.391737681818182)
Predict: 0.0
Else (feature 1 > -7.391737681818182)
Predict: 2.0
Else (feature 0 > -7.16007390909091)
If (feature 5 <= 1.468512909467716)
Predict: 1.0
Else (feature 5 > 1.468512909467716)
Predict: 0.0
Else (feature 2 > -1.21523575)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -17.560554999999994)
Predict: 0.0
Else (feature 1 > -17.560554999999994)
If (feature 0 <= 3.5434871363636367)
If (feature 4 <= 2.884105112415355)
Predict: 2.0
Else (feature 4 > 2.884105112415355)
If (feature 5 <= 1.2763838461753882)
Predict: 0.0
Else (feature 5 > 1.2763838461753882)
Predict: 2.0
Else (feature 0 > 3.5434871363636367)
Predict: 0.0
Else (feature 1 > -6.928410727272727)
If (feature 1 <= 1.3574864545454546)
Predict: 0.0
Else (feature 1 > 1.3574864545454546)
If (feature 0 <= -3.125991409090909)
If (feature 0 <= -5.033296227272727)
Predict: 0.0
Else (feature 0 > -5.033296227272727)
Predict: 2.0
Else (feature 0 > -3.125991409090909)
Predict: 0.0
Else (feature 5 > 2.5963437348657123)
If (feature 0 <= 1.2947807727272727)
If (feature 2 <= -2.184857272727273)
If (feature 3 <= 2.29340842981981)
If (feature 2 <= -2.8084323636363635)
If (feature 0 <= -0.3738945909090909)
Predict: 2.0
Else (feature 0 > -0.3738945909090909)
If (feature 4 <= 1.6647103963937675)
Predict: 0.0
Else (feature 4 > 1.6647103963937675)
Predict: 2.0
Else (feature 2 > -2.8084323636363635)
If (feature 5 <= 3.803765702041731)
Predict: 4.0
Else (feature 5 > 3.803765702041731)
Predict: 0.0
Else (feature 3 > 2.29340842981981)
Predict: 0.0
Else (feature 2 > -2.184857272727273)
If (feature 3 <= 3.6472443306163704)
If (feature 3 <= 1.3680974461096451)
If (feature 0 <= -1.488667)
Predict: 0.0
Else (feature 0 > -1.488667)
Predict: 3.0
Else (feature 3 > 1.3680974461096451)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -1.2012645454545452)
If (feature 5 <= 5.9923123904255196)
Predict: 2.0
Else (feature 5 > 5.9923123904255196)
Predict: 0.0
Else (feature 1 > -1.2012645454545452)
If (feature 0 <= -5.033296227272727)
Predict: 2.0
Else (feature 0 > -5.033296227272727)
If (feature 3 <= 2.06138891346951)
Predict: 2.0
Else (feature 3 > 2.06138891346951)
Predict: 0.0
Else (feature 3 > 3.6472443306163704)
If (feature 5 <= 2.815074767092767)
If (feature 4 <= 2.258741828976616)
Predict: 0.0
Else (feature 4 > 2.258741828976616)
If (feature 1 <= -8.71901181818182)
Predict: 2.0
Else (feature 1 > -8.71901181818182)
Predict: 0.0
Else (feature 5 > 2.815074767092767)
Predict: 0.0
Else (feature 0 > 1.2947807727272727)
If (feature 5 <= 2.815074767092767)
If (feature 0 <= 2.0507363636363634)
If (feature 1 <= -9.513290454545455)
Predict: 2.0
Else (feature 1 > -9.513290454545455)
Predict: 0.0
Else (feature 0 > 2.0507363636363634)
Predict: 0.0
Else (feature 5 > 2.815074767092767)
Predict: 0.0
Else (feature 4 > 3.509467511028114)
If (feature 4 <= 5.856212462090488)
If (feature 3 <= 3.2533446617122705)
If (feature 1 <= -8.168595136363637)
If (feature 0 <= -2.0286351363636363)
If (feature 3 <= 1.6040286540977868)
Predict: 4.0
Else (feature 3 > 1.6040286540977868)
If (feature 5 <= 4.209746983589046)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -13.949734999999999)
Predict: 4.0
Else (feature 1 > -13.949734999999999)
If (feature 4 <= 5.395001662922967)
Predict: 0.0
Else (feature 4 > 5.395001662922967)
If (feature 0 <= -3.125991409090909)
Predict: 0.0
Else (feature 0 > -3.125991409090909)
Predict: 4.0
Else (feature 5 > 4.209746983589046)
Predict: 2.0
Else (feature 0 > -2.0286351363636363)
If (feature 0 <= 2.8798476363636363)
If (feature 1 <= -11.699289545454546)
Predict: 0.0
Else (feature 1 > -11.699289545454546)
If (feature 5 <= 0.1803188998830109)
Predict: 1.0
Else (feature 5 > 0.1803188998830109)
If (feature 2 <= 1.0950141818181818)
If (feature 5 <= 4.852637451173388)
If (feature 0 <= -1.488667)
If (feature 1 <= -9.102216772727274)
Predict: 2.0
Else (feature 1 > -9.102216772727274)
If (feature 1 <= -8.71901181818182)
Predict: 0.0
Else (feature 1 > -8.71901181818182)
Predict: 4.0
Else (feature 0 > -1.488667)
Predict: 2.0
Else (feature 5 > 4.852637451173388)
Predict: 4.0
Else (feature 2 > 1.0950141818181818)
Predict: 0.0
Else (feature 0 > 2.8798476363636363)
Predict: 0.0
Else (feature 1 > -8.168595136363637)
If (feature 3 <= 1.6040286540977868)
If (feature 1 <= -6.928410727272727)
Predict: 4.0
Else (feature 1 > -6.928410727272727)
If (feature 4 <= 3.987483559659423)
If (feature 0 <= -1.488667)
Predict: 0.0
Else (feature 0 > -1.488667)
Predict: 4.0
Else (feature 4 > 3.987483559659423)
Predict: 0.0
Else (feature 3 > 1.6040286540977868)
If (feature 1 <= -7.811519545454546)
If (feature 4 <= 4.517274572453781)
Predict: 2.0
Else (feature 4 > 4.517274572453781)
If (feature 3 <= 2.499281243538517)
Predict: 2.0
Else (feature 3 > 2.499281243538517)
If (feature 5 <= 2.041048981215627)
Predict: 0.0
Else (feature 5 > 2.041048981215627)
Predict: 2.0
Else (feature 1 > -7.811519545454546)
If (feature 2 <= 1.0950141818181818)
If (feature 0 <= 0.32806340909090914)
If (feature 3 <= 1.8104608428061633)
If (feature 4 <= 5.01193131655486)
Predict: 0.0
Else (feature 4 > 5.01193131655486)
Predict: 4.0
Else (feature 3 > 1.8104608428061633)
Predict: 0.0
Else (feature 0 > 0.32806340909090914)
If (feature 5 <= 1.763631215969657)
If (feature 4 <= 4.517274572453781)
Predict: 0.0
Else (feature 4 > 4.517274572453781)
Predict: 4.0
Else (feature 5 > 1.763631215969657)
If (feature 0 <= 0.9951855113636363)
If (feature 5 <= 2.815074767092767)
Predict: 2.0
Else (feature 5 > 2.815074767092767)
Predict: 0.0
Else (feature 0 > 0.9951855113636363)
Predict: 0.0
Else (feature 2 > 1.0950141818181818)
If (feature 3 <= 2.713415564122834)
Predict: 0.0
Else (feature 3 > 2.713415564122834)
Predict: 2.0
Else (feature 3 > 3.2533446617122705)
If (feature 2 <= -1.21523575)
If (feature 0 <= 2.8798476363636363)
If (feature 5 <= 3.803765702041731)
If (feature 0 <= -1.2779045454545455)
Predict: 0.0
Else (feature 0 > -1.2779045454545455)
If (feature 0 <= -0.7518725909090909)
Predict: 2.0
Else (feature 0 > -0.7518725909090909)
Predict: 0.0
Else (feature 5 > 3.803765702041731)
If (feature 2 <= -1.7981704545454544)
Predict: 0.0
Else (feature 2 > -1.7981704545454544)
If (feature 1 <= -7.391737681818182)
If (feature 5 <= 4.852637451173388)
Predict: 2.0
Else (feature 5 > 4.852637451173388)
Predict: 0.0
Else (feature 1 > -7.391737681818182)
Predict: 0.0
Else (feature 0 > 2.8798476363636363)
If (feature 2 <= -1.7981704545454544)
Predict: 0.0
Else (feature 2 > -1.7981704545454544)
If (feature 1 <= -13.949734999999999)
Predict: 0.0
Else (feature 1 > -13.949734999999999)
If (feature 3 <= 5.04993896073149)
Predict: 0.0
Else (feature 3 > 5.04993896073149)
If (feature 0 <= 5.987275954545455)
Predict: 4.0
Else (feature 0 > 5.987275954545455)
Predict: 0.0
Else (feature 2 > -1.21523575)
If (feature 5 <= 1.9019736832530727)
If (feature 4 <= 5.395001662922967)
If (feature 4 <= 3.987483559659423)
If (feature 0 <= -1.488667)
If (feature 2 <= 0.6160089999999999)
If (feature 0 <= -6.216001363636364)
Predict: 0.0
Else (feature 0 > -6.216001363636364)
Predict: 2.0
Else (feature 2 > 0.6160089999999999)
Predict: 2.0
Else (feature 0 > -1.488667)
Predict: 0.0
Else (feature 4 > 3.987483559659423)
If (feature 0 <= 3.5434871363636367)
Predict: 0.0
Else (feature 0 > 3.5434871363636367)
Predict: 4.0
Else (feature 4 > 5.395001662922967)
If (feature 5 <= 1.2763838461753882)
Predict: 0.0
Else (feature 5 > 1.2763838461753882)
Predict: 4.0
Else (feature 5 > 1.9019736832530727)
If (feature 3 <= 5.89679045291164)
If (feature 1 <= -10.122931363636361)
Predict: 0.0
Else (feature 1 > -10.122931363636361)
If (feature 1 <= -6.371024409090909)
If (feature 0 <= -1.488667)
If (feature 4 <= 3.987483559659423)
Predict: 2.0
Else (feature 4 > 3.987483559659423)
If (feature 2 <= 1.6994305454545455)
If (feature 2 <= -0.5214064090909091)
If (feature 5 <= 3.104356065106381)
Predict: 0.0
Else (feature 5 > 3.104356065106381)
Predict: 2.0
Else (feature 2 > -0.5214064090909091)
Predict: 0.0
Else (feature 2 > 1.6994305454545455)
Predict: 2.0
Else (feature 0 > -1.488667)
Predict: 2.0
Else (feature 1 > -6.371024409090909)
If (feature 4 <= 5.395001662922967)
If (feature 0 <= -4.420172227272728)
If (feature 1 <= -0.16313340909090912)
If (feature 5 <= 3.803765702041731)
Predict: 0.0
Else (feature 5 > 3.803765702041731)
If (feature 0 <= -6.216001363636364)
Predict: 2.0
Else (feature 0 > -6.216001363636364)
Predict: 0.0
Else (feature 1 > -0.16313340909090912)
If (feature 0 <= -5.033296227272727)
Predict: 0.0
Else (feature 0 > -5.033296227272727)
Predict: 2.0
Else (feature 0 > -4.420172227272728)
Predict: 0.0
Else (feature 4 > 5.395001662922967)
If (feature 0 <= -2.5738281818181816)
If (feature 0 <= -3.7931120454545457)
Predict: 0.0
Else (feature 0 > -3.7931120454545457)
Predict: 2.0
Else (feature 0 > -2.5738281818181816)
If (feature 2 <= -1.0073793636363635)
Predict: 4.0
Else (feature 2 > -1.0073793636363635)
Predict: 0.0
Else (feature 3 > 5.89679045291164)
Predict: 0.0
Else (feature 4 > 5.856212462090488)
If (feature 5 <= 2.815074767092767)
If (feature 4 <= 7.749673922326561)
If (feature 3 <= 8.669590934585441)
If (feature 1 <= -9.359573909090908)
If (feature 5 <= 1.468512909467716)
If (feature 2 <= -0.6868815000000001)
If (feature 0 <= -2.5738281818181816)
Predict: 4.0
Else (feature 0 > -2.5738281818181816)
Predict: 2.0
Else (feature 2 > -0.6868815000000001)
If (feature 4 <= 7.040265431372278)
Predict: 0.0
Else (feature 4 > 7.040265431372278)
Predict: 4.0
Else (feature 5 > 1.468512909467716)
If (feature 2 <= -5.8148077727272725)
Predict: 2.0
Else (feature 2 > -5.8148077727272725)
If (feature 3 <= 2.06138891346951)
If (feature 4 <= 6.293679874885813)
If (feature 3 <= 1.3680974461096451)
Predict: 0.0
Else (feature 3 > 1.3680974461096451)
Predict: 4.0
Else (feature 4 > 6.293679874885813)
Predict: 0.0
Else (feature 3 > 2.06138891346951)
Predict: 0.0
Else (feature 1 > -9.359573909090908)
If (feature 1 <= -1.2012645454545452)
If (feature 0 <= -5.7328163636363625)
If (feature 4 <= 6.293679874885813)
If (feature 2 <= -1.4916079545454544)
Predict: 0.0
Else (feature 2 > -1.4916079545454544)
If (feature 0 <= -6.216001363636364)
Predict: 0.0
Else (feature 0 > -6.216001363636364)
Predict: 4.0
Else (feature 4 > 6.293679874885813)
If (feature 2 <= -1.7981704545454544)
Predict: 0.0
Else (feature 2 > -1.7981704545454544)
Predict: 4.0
Else (feature 0 > -5.7328163636363625)
If (feature 2 <= -5.8148077727272725)
Predict: 0.0
Else (feature 2 > -5.8148077727272725)
If (feature 1 <= -7.811519545454546)
If (feature 0 <= -3.7931120454545457)
If (feature 3 <= 3.2533446617122705)
Predict: 0.0
Else (feature 3 > 3.2533446617122705)
Predict: 4.0
Else (feature 0 > -3.7931120454545457)
If (feature 2 <= 1.6994305454545455)
If (feature 0 <= 3.5434871363636367)
If (feature 5 <= 0.80494691075511)
Predict: 0.0
Else (feature 5 > 0.80494691075511)
If (feature 3 <= 3.2533446617122705)
If (feature 2 <= -1.7981704545454544)
Predict: 4.0
Else (feature 2 > -1.7981704545454544)
If (feature 5 <= 2.178632830786455)
If (feature 0 <= 2.0507363636363634)
Predict: 0.0
Else (feature 0 > 2.0507363636363634)
Predict: 4.0
Else (feature 5 > 2.178632830786455)
Predict: 0.0
Else (feature 3 > 3.2533446617122705)
Predict: 0.0
Else (feature 0 > 3.5434871363636367)
Predict: 0.0
Else (feature 2 > 1.6994305454545455)
Predict: 2.0
*** WARNING: skipped 319338 bytes of output ***
Predict: 0.0
Else (feature 4 > 2.258741828976616)
If (feature 5 <= 2.5963437348657123)
If (feature 0 <= -3.125991409090909)
If (feature 3 <= 4.165661639629755)
If (feature 4 <= 2.884105112415355)
If (feature 3 <= 2.713415564122834)
If (feature 5 <= 1.6145607532142021)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 5 > 1.6145607532142021)
Predict: 2.0
Else (feature 3 > 2.713415564122834)
Predict: 0.0
Else (feature 4 > 2.884105112415355)
If (feature 3 <= 1.3680974461096451)
If (feature 1 <= -2.4344832727272725)
If (feature 5 <= 1.9019736832530727)
Predict: 4.0
Else (feature 5 > 1.9019736832530727)
Predict: 2.0
Else (feature 1 > -2.4344832727272725)
Predict: 0.0
Else (feature 3 > 1.3680974461096451)
If (feature 2 <= 1.6994305454545455)
If (feature 2 <= 0.1979699090909091)
Predict: 0.0
Else (feature 2 > 0.1979699090909091)
If (feature 3 <= 3.6472443306163704)
Predict: 0.0
Else (feature 3 > 3.6472443306163704)
Predict: 4.0
Else (feature 2 > 1.6994305454545455)
Predict: 2.0
Else (feature 3 > 4.165661639629755)
If (feature 4 <= 5.856212462090488)
If (feature 4 <= 3.987483559659423)
If (feature 1 <= -9.513290454545455)
If (feature 5 <= 1.2763838461753882)
Predict: 0.0
Else (feature 5 > 1.2763838461753882)
If (feature 3 <= 4.655894911349467)
Predict: 0.0
Else (feature 3 > 4.655894911349467)
Predict: 2.0
Else (feature 1 > -9.513290454545455)
Predict: 0.0
Else (feature 4 > 3.987483559659423)
Predict: 0.0
Else (feature 4 > 5.856212462090488)
If (feature 2 <= -1.21523575)
Predict: 0.0
Else (feature 2 > -1.21523575)
Predict: 4.0
Else (feature 0 > -3.125991409090909)
If (feature 2 <= 1.0950141818181818)
If (feature 0 <= 2.0507363636363634)
If (feature 2 <= -1.7981704545454544)
If (feature 2 <= -5.8148077727272725)
If (feature 4 <= 6.684353189725881)
If (feature 0 <= -1.488667)
If (feature 1 <= -9.513290454545455)
Predict: 2.0
Else (feature 1 > -9.513290454545455)
Predict: 0.0
Else (feature 0 > -1.488667)
Predict: 2.0
Else (feature 4 > 6.684353189725881)
Predict: 0.0
Else (feature 2 > -5.8148077727272725)
If (feature 1 <= -4.157154727272728)
If (feature 4 <= 3.509467511028114)
If (feature 0 <= -2.5738281818181816)
Predict: 2.0
Else (feature 0 > -2.5738281818181816)
If (feature 5 <= 2.374697522458404)
Predict: 0.0
Else (feature 5 > 2.374697522458404)
Predict: 2.0
Else (feature 4 > 3.509467511028114)
If (feature 0 <= -0.3738945909090909)
If (feature 3 <= 1.8104608428061633)
Predict: 4.0
Else (feature 3 > 1.8104608428061633)
If (feature 4 <= 5.395001662922967)
Predict: 0.0
Else (feature 4 > 5.395001662922967)
Predict: 4.0
Else (feature 0 > -0.3738945909090909)
If (feature 4 <= 5.856212462090488)
Predict: 2.0
Else (feature 4 > 5.856212462090488)
Predict: 4.0
Else (feature 1 > -4.157154727272728)
Predict: 0.0
Else (feature 2 > -1.7981704545454544)
If (feature 1 <= -7.811519545454546)
If (feature 3 <= 1.1186607583073207)
Predict: 1.0
Else (feature 3 > 1.1186607583073207)
Predict: 2.0
Else (feature 1 > -7.811519545454546)
If (feature 1 <= -5.200512090909092)
If (feature 3 <= 2.06138891346951)
Predict: 4.0
Else (feature 3 > 2.06138891346951)
If (feature 2 <= 0.6160089999999999)
If (feature 0 <= 0.9951855113636363)
Predict: 0.0
Else (feature 0 > 0.9951855113636363)
If (feature 5 <= 1.076775279931446)
Predict: 0.0
Else (feature 5 > 1.076775279931446)
Predict: 4.0
Else (feature 2 > 0.6160089999999999)
If (feature 0 <= -1.488667)
Predict: 2.0
Else (feature 0 > -1.488667)
Predict: 4.0
Else (feature 1 > -5.200512090909092)
If (feature 5 <= 1.076775279931446)
If (feature 3 <= 2.29340842981981)
Predict: 2.0
Else (feature 3 > 2.29340842981981)
Predict: 4.0
Else (feature 5 > 1.076775279931446)
If (feature 0 <= 1.2947807727272727)
Predict: 0.0
Else (feature 0 > 1.2947807727272727)
If (feature 3 <= 1.6040286540977868)
Predict: 4.0
Else (feature 3 > 1.6040286540977868)
Predict: 0.0
Else (feature 0 > 2.0507363636363634)
If (feature 4 <= 5.01193131655486)
If (feature 1 <= -13.949734999999999)
Predict: 0.0
Else (feature 1 > -13.949734999999999)
If (feature 3 <= 3.2533446617122705)
If (feature 1 <= -7.391737681818182)
Predict: 2.0
Else (feature 1 > -7.391737681818182)
Predict: 0.0
Else (feature 3 > 3.2533446617122705)
Predict: 0.0
Else (feature 4 > 5.01193131655486)
If (feature 1 <= -8.71901181818182)
If (feature 2 <= -5.8148077727272725)
Predict: 2.0
Else (feature 2 > -5.8148077727272725)
Predict: 0.0
Else (feature 1 > -8.71901181818182)
If (feature 3 <= 8.669590934585441)
If (feature 1 <= -1.2012645454545452)
If (feature 3 <= 1.6040286540977868)
Predict: 0.0
Else (feature 3 > 1.6040286540977868)
If (feature 4 <= 9.024711300064961)
If (feature 4 <= 8.135808679557085)
If (feature 1 <= -8.649340000000002)
If (feature 0 <= 2.8798476363636363)
Predict: 4.0
Else (feature 0 > 2.8798476363636363)
Predict: 0.0
Else (feature 1 > -8.649340000000002)
If (feature 5 <= 2.178632830786455)
If (feature 0 <= 5.987275954545455)
If (feature 4 <= 7.040265431372278)
If (feature 1 <= -6.371024409090909)
If (feature 5 <= 1.076775279931446)
Predict: 4.0
Else (feature 5 > 1.076775279931446)
Predict: 0.0
Else (feature 1 > -6.371024409090909)
Predict: 4.0
Else (feature 4 > 7.040265431372278)
Predict: 4.0
Else (feature 0 > 5.987275954545455)
If (feature 4 <= 6.293679874885813)
If (feature 5 <= 1.9019736832530727)
Predict: 4.0
Else (feature 5 > 1.9019736832530727)
Predict: 0.0
Else (feature 4 > 6.293679874885813)
Predict: 4.0
Else (feature 5 > 2.178632830786455)
If (feature 2 <= -0.6868815000000001)
If (feature 3 <= 2.713415564122834)
Predict: 0.0
Else (feature 3 > 2.713415564122834)
If (feature 4 <= 6.293679874885813)
If (feature 0 <= 5.987275954545455)
Predict: 4.0
Else (feature 0 > 5.987275954545455)
If (feature 4 <= 5.395001662922967)
Predict: 4.0
Else (feature 4 > 5.395001662922967)
Predict: 0.0
Else (feature 4 > 6.293679874885813)
Predict: 4.0
Else (feature 2 > -0.6868815000000001)
If (feature 0 <= 3.5434871363636367)
Predict: 4.0
Else (feature 0 > 3.5434871363636367)
Predict: 0.0
Else (feature 4 > 8.135808679557085)
If (feature 3 <= 2.29340842981981)
Predict: 0.0
Else (feature 3 > 2.29340842981981)
If (feature 3 <= 4.165661639629755)
Predict: 4.0
Else (feature 3 > 4.165661639629755)
If (feature 3 <= 6.530604828890175)
Predict: 0.0
Else (feature 3 > 6.530604828890175)
Predict: 4.0
Else (feature 4 > 9.024711300064961)
Predict: 0.0
Else (feature 1 > -1.2012645454545452)
Predict: 0.0
Else (feature 3 > 8.669590934585441)
Predict: 0.0
Else (feature 2 > 1.0950141818181818)
If (feature 2 <= 1.6994305454545455)
If (feature 1 <= -5.200512090909092)
If (feature 4 <= 5.01193131655486)
Predict: 2.0
Else (feature 4 > 5.01193131655486)
If (feature 0 <= 2.8798476363636363)
If (feature 3 <= 2.952235990607485)
Predict: 4.0
Else (feature 3 > 2.952235990607485)
If (feature 4 <= 5.856212462090488)
Predict: 2.0
Else (feature 4 > 5.856212462090488)
Predict: 0.0
Else (feature 0 > 2.8798476363636363)
Predict: 0.0
Else (feature 1 > -5.200512090909092)
Predict: 0.0
Else (feature 2 > 1.6994305454545455)
If (feature 0 <= -2.0286351363636363)
If (feature 5 <= 2.178632830786455)
Predict: 2.0
Else (feature 5 > 2.178632830786455)
Predict: 0.0
Else (feature 0 > -2.0286351363636363)
If (feature 4 <= 8.135808679557085)
Predict: 0.0
Else (feature 4 > 8.135808679557085)
If (feature 3 <= 2.952235990607485)
If (feature 0 <= -0.16835886363636363)
Predict: 0.0
Else (feature 0 > -0.16835886363636363)
Predict: 4.0
Else (feature 3 > 2.952235990607485)
Predict: 0.0
Else (feature 5 > 2.5963437348657123)
If (feature 4 <= 4.517274572453781)
If (feature 3 <= 3.2533446617122705)
If (feature 1 <= -4.157154727272728)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -17.560554999999994)
Predict: 0.0
Else (feature 1 > -17.560554999999994)
If (feature 3 <= 1.1186607583073207)
Predict: 4.0
Else (feature 3 > 1.1186607583073207)
If (feature 1 <= -6.928410727272727)
Predict: 2.0
Else (feature 1 > -6.928410727272727)
If (feature 4 <= 3.509467511028114)
Predict: 2.0
Else (feature 4 > 3.509467511028114)
Predict: 0.0
Else (feature 1 > -4.157154727272728)
If (feature 2 <= 1.0950141818181818)
Predict: 0.0
Else (feature 2 > 1.0950141818181818)
Predict: 2.0
Else (feature 3 > 3.2533446617122705)
If (feature 0 <= -0.7518725909090909)
If (feature 3 <= 3.6472443306163704)
If (feature 2 <= 0.1979699090909091)
If (feature 0 <= -2.5738281818181816)
Predict: 0.0
Else (feature 0 > -2.5738281818181816)
Predict: 2.0
Else (feature 2 > 0.1979699090909091)
Predict: 2.0
Else (feature 3 > 3.6472443306163704)
If (feature 5 <= 2.815074767092767)
If (feature 2 <= 0.1979699090909091)
Predict: 0.0
Else (feature 2 > 0.1979699090909091)
If (feature 4 <= 3.987483559659423)
Predict: 2.0
Else (feature 4 > 3.987483559659423)
Predict: 0.0
Else (feature 5 > 2.815074767092767)
If (feature 3 <= 4.655894911349467)
If (feature 4 <= 3.987483559659423)
Predict: 0.0
Else (feature 4 > 3.987483559659423)
If (feature 1 <= -7.811519545454546)
If (feature 2 <= -1.4916079545454544)
Predict: 2.0
Else (feature 2 > -1.4916079545454544)
Predict: 0.0
Else (feature 1 > -7.811519545454546)
Predict: 0.0
Else (feature 3 > 4.655894911349467)
Predict: 0.0
Else (feature 0 > -0.7518725909090909)
Predict: 0.0
Else (feature 4 > 4.517274572453781)
If (feature 3 <= 4.165661639629755)
If (feature 0 <= 2.0507363636363634)
If (feature 5 <= 5.9923123904255196)
If (feature 2 <= -1.7981704545454544)
If (feature 1 <= -4.157154727272728)
If (feature 1 <= -7.391737681818182)
If (feature 4 <= 7.040265431372278)
If (feature 2 <= -5.8148077727272725)
If (feature 3 <= 2.29340842981981)
Predict: 2.0
Else (feature 3 > 2.29340842981981)
Predict: 0.0
Else (feature 2 > -5.8148077727272725)
If (feature 5 <= 3.465871724305953)
If (feature 0 <= -2.5738281818181816)
Predict: 4.0
Else (feature 0 > -2.5738281818181816)
Predict: 2.0
Else (feature 5 > 3.465871724305953)
Predict: 2.0
Else (feature 4 > 7.040265431372278)
If (feature 5 <= 2.815074767092767)
Predict: 4.0
Else (feature 5 > 2.815074767092767)
Predict: 0.0
Else (feature 1 > -7.391737681818182)
If (feature 3 <= 1.6040286540977868)
Predict: 0.0
Else (feature 3 > 1.6040286540977868)
If (feature 0 <= -3.125991409090909)
Predict: 0.0
Else (feature 0 > -3.125991409090909)
Predict: 4.0
Else (feature 1 > -4.157154727272728)
Predict: 0.0
Else (feature 2 > -1.7981704545454544)
If (feature 1 <= -4.157154727272728)
If (feature 5 <= 3.803765702041731)
If (feature 5 <= 3.465871724305953)
If (feature 1 <= -5.8293135909090905)
If (feature 4 <= 6.293679874885813)
If (feature 0 <= -3.125991409090909)
Predict: 0.0
Else (feature 0 > -3.125991409090909)
If (feature 2 <= 0.6160089999999999)
Predict: 2.0
Else (feature 2 > 0.6160089999999999)
Predict: 0.0
Else (feature 4 > 6.293679874885813)
If (feature 5 <= 2.815074767092767)
Predict: 0.0
Else (feature 5 > 2.815074767092767)
Predict: 2.0
Else (feature 1 > -5.8293135909090905)
If (feature 2 <= -1.073567681818182)
Predict: 4.0
Else (feature 2 > -1.073567681818182)
If (feature 4 <= 5.01193131655486)
Predict: 0.0
Else (feature 4 > 5.01193131655486)
If (feature 4 <= 6.684353189725881)
Predict: 2.0
Else (feature 4 > 6.684353189725881)
Predict: 0.0
Else (feature 5 > 3.465871724305953)
Predict: 2.0
Else (feature 5 > 3.803765702041731)
If (feature 3 <= 2.499281243538517)
If (feature 4 <= 7.374296497627369)
Predict: 2.0
Else (feature 4 > 7.374296497627369)
If (feature 4 <= 8.135808679557085)
Predict: 0.0
Else (feature 4 > 8.135808679557085)
Predict: 4.0
Else (feature 3 > 2.499281243538517)
Predict: 0.0
Else (feature 1 > -4.157154727272728)
If (feature 1 <= -2.4344832727272725)
If (feature 4 <= 7.040265431372278)
Predict: 0.0
Else (feature 4 > 7.040265431372278)
Predict: 4.0
Else (feature 1 > -2.4344832727272725)
Predict: 0.0
Else (feature 5 > 5.9923123904255196)
If (feature 5 <= 8.512274217664926)
If (feature 0 <= 0.32806340909090914)
Predict: 0.0
Else (feature 0 > 0.32806340909090914)
If (feature 1 <= -5.200512090909092)
If (feature 2 <= -3.574838818181818)
Predict: 4.0
Else (feature 2 > -3.574838818181818)
Predict: 0.0
Else (feature 1 > -5.200512090909092)
Predict: 0.0
Else (feature 5 > 8.512274217664926)
Predict: 0.0
Else (feature 0 > 2.0507363636363634)
If (feature 2 <= -5.8148077727272725)
If (feature 3 <= 1.6040286540977868)
Predict: 2.0
Else (feature 3 > 1.6040286540977868)
Predict: 0.0
Else (feature 2 > -5.8148077727272725)
If (feature 3 <= 3.6472443306163704)
Predict: 0.0
Else (feature 3 > 3.6472443306163704)
If (feature 1 <= -6.371024409090909)
Predict: 0.0
Else (feature 1 > -6.371024409090909)
If (feature 1 <= -5.200512090909092)
Predict: 4.0
Else (feature 1 > -5.200512090909092)
Predict: 0.0
Else (feature 3 > 4.165661639629755)
If (feature 5 <= 2.815074767092767)
If (feature 0 <= -2.5738281818181816)
Predict: 0.0
Else (feature 0 > -2.5738281818181816)
If (feature 2 <= -0.6868815000000001)
If (feature 3 <= 8.669590934585441)
If (feature 0 <= 7.339635468181818)
If (feature 4 <= 7.749673922326561)
If (feature 0 <= 5.987275954545455)
Predict: 4.0
Else (feature 0 > 5.987275954545455)
If (feature 3 <= 7.301299272895662)
Predict: 4.0
Else (feature 3 > 7.301299272895662)
Predict: 0.0
Else (feature 4 > 7.749673922326561)
Predict: 0.0
Else (feature 0 > 7.339635468181818)
Predict: 0.0
Else (feature 3 > 8.669590934585441)
Predict: 0.0
Else (feature 2 > -0.6868815000000001)
If (feature 4 <= 7.749673922326561)
Predict: 0.0
Else (feature 4 > 7.749673922326561)
If (feature 3 <= 5.45309454568844)
Predict: 4.0
Else (feature 3 > 5.45309454568844)
Predict: 0.0
Else (feature 5 > 2.815074767092767)
If (feature 3 <= 5.45309454568844)
If (feature 1 <= -7.811519545454546)
If (feature 0 <= -4.420172227272728)
If (feature 5 <= 3.465871724305953)
If (feature 0 <= -7.16007390909091)
Predict: 0.0
Else (feature 0 > -7.16007390909091)
Predict: 2.0
Else (feature 5 > 3.465871724305953)
Predict: 0.0
Else (feature 0 > -4.420172227272728)
If (feature 4 <= 7.749673922326561)
If (feature 5 <= 4.209746983589046)
If (feature 3 <= 5.04993896073149)
Predict: 0.0
Else (feature 3 > 5.04993896073149)
If (feature 2 <= -0.5214064090909091)
Predict: 2.0
Else (feature 2 > -0.5214064090909091)
Predict: 0.0
Else (feature 5 > 4.209746983589046)
Predict: 0.0
Else (feature 4 > 7.749673922326561)
If (feature 2 <= -2.8084323636363635)
If (feature 1 <= -9.513290454545455)
Predict: 0.0
Else (feature 1 > -9.513290454545455)
If (feature 0 <= 2.8798476363636363)
Predict: 4.0
Else (feature 0 > 2.8798476363636363)
Predict: 0.0
Else (feature 2 > -2.8084323636363635)
If (feature 1 <= -11.699289545454546)
If (feature 5 <= 4.209746983589046)
If (feature 0 <= -2.0286351363636363)
Predict: 0.0
Else (feature 0 > -2.0286351363636363)
Predict: 2.0
Else (feature 5 > 4.209746983589046)
Predict: 0.0
Else (feature 1 > -11.699289545454546)
Predict: 0.0
Else (feature 1 > -7.811519545454546)
If (feature 0 <= 2.8798476363636363)
If (feature 4 <= 7.040265431372278)
If (feature 3 <= 5.04993896073149)
If (feature 0 <= -1.488667)
Predict: 0.0
Else (feature 0 > -1.488667)
If (feature 0 <= -0.7518725909090909)
Predict: 2.0
Else (feature 0 > -0.7518725909090909)
Predict: 0.0
Else (feature 3 > 5.04993896073149)
If (feature 5 <= 3.803765702041731)
Predict: 0.0
Else (feature 5 > 3.803765702041731)
Predict: 2.0
Else (feature 4 > 7.040265431372278)
If (feature 4 <= 9.024711300064961)
If (feature 4 <= 7.374296497627369)
If (feature 0 <= 0.9951855113636363)
Predict: 4.0
Else (feature 0 > 0.9951855113636363)
Predict: 0.0
Else (feature 4 > 7.374296497627369)
If (feature 2 <= -5.8148077727272725)
Predict: 0.0
Else (feature 2 > -5.8148077727272725)
Predict: 4.0
Else (feature 4 > 9.024711300064961)
Predict: 0.0
Else (feature 0 > 2.8798476363636363)
Predict: 0.0
Else (feature 3 > 5.45309454568844)
If (feature 1 <= -5.8293135909090905)
If (feature 4 <= 8.135808679557085)
Predict: 0.0
Else (feature 4 > 8.135808679557085)
If (feature 3 <= 5.89679045291164)
If (feature 5 <= 4.852637451173388)
If (feature 5 <= 3.465871724305953)
Predict: 0.0
Else (feature 5 > 3.465871724305953)
Predict: 4.0
Else (feature 5 > 4.852637451173388)
Predict: 0.0
Else (feature 3 > 5.89679045291164)
Predict: 0.0
Else (feature 1 > -5.8293135909090905)
If (feature 3 <= 7.301299272895662)
If (feature 0 <= 0.9951855113636363)
Predict: 0.0
Else (feature 0 > 0.9951855113636363)
If (feature 4 <= 5.856212462090488)
Predict: 0.0
Else (feature 4 > 5.856212462090488)
If (feature 0 <= 5.987275954545455)
If (feature 5 <= 3.465871724305953)
Predict: 4.0
Else (feature 5 > 3.465871724305953)
Predict: 0.0
Else (feature 0 > 5.987275954545455)
Predict: 0.0
Else (feature 3 > 7.301299272895662)
If (feature 1 <= -5.200512090909092)
If (feature 4 <= 7.374296497627369)
Predict: 0.0
Else (feature 4 > 7.374296497627369)
If (feature 5 <= 3.104356065106381)
Predict: 4.0
Else (feature 5 > 3.104356065106381)
Predict: 0.0
Else (feature 1 > -5.200512090909092)
Predict: 0.0
import org.apache.spark.ml.classification.RandomForestClassificationModel
rfModel: org.apache.spark.ml.classification.RandomForestClassificationModel = RandomForestClassificationModel: uid=rfc_794232dabcdf, numTrees=10, numClasses=5, numFeatures=6
Save and Load later to Serve Predictions
When you are satisfied with the model you have trained/fitted then you can save it to a file and load it again from another low-latency prediciton-serving system to serve predictions.
model
res27: org.apache.spark.ml.PipelineModel = pipeline_76d41a70ccc7
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.PipelineModel
Save the model to a file in stable storage.
model.write.overwrite().save("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
Load the model, typically from a prediction or model serving layer/system.
val same_rfModelFromSavedFile = PipelineModel.load("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
same_rfModelFromSavedFile: org.apache.spark.ml.PipelineModel = pipeline_76d41a70ccc7
same_rfModelFromSavedFile
res29: org.apache.spark.ml.PipelineModel = pipeline_76d41a70ccc7
Suppose new data is coming at us and we need to serve our predicitons of the activities
Note: We only need accelerometer readings so prediciton pipeline can be simplified to only take in the needed features for prediction. For simplicity let's assume the test data is representative of the new data (previously unseen) that is coming at us for serving our activity predicitons.
val newDataForPrediction = testData.sample(0.0004,12348L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|
| -2.504156 | -9.297302727272728 | -1.3522605454545453 | 2.1172874206915644 | 1.2430011314330338 | 2.6961258744053342 |
Here is another new data coming at you... you can predict the current activity being done by simply transforming the loaded model (estimator).
val newDataForPrediction = testData.sample(0.0004,1234L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|
| 0.4465079090909091 | -0.6821994545454545 | 9.447101818181817 | 8.440257552550214e-2 | 0.10589937675019803 | 6.67451371767209e-2 |
display(same_rfModelFromSavedFile.transform(newDataForPrediction).select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction"))
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ | prediction |
|---|---|---|---|---|---|---|
| 0.4465079090909091 | -0.6821994545454545 | 9.447101818181817 | 8.440257552550214e-2 | 0.10589937675019803 | 6.67451371767209e-2 | 3.0 |
Some minor post-processing to connect prediction label to the activity string.
Typically one prediction is just a small part of larger business or science use-case.
activityLabelDF.show
+--------+-----+
|activity|label|
+--------+-----+
| Jogging| 0.0|
| Jumping| 4.0|
|Standing| 1.0|
| Walking| 2.0|
| Sitting| 3.0|
+--------+-----+
display(same_rfModelFromSavedFile
.transform(newDataForPrediction)
.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction")
.join(activityLabelDF,$"prediction"===$"label")
.drop("prediction","label")
.withColumnRenamed("activity","Your current activity is likely to be")
)
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ | Your current activity is likely to be |
|---|---|---|---|---|---|---|
| 0.4465079090909091 | -0.6821994545454545 | 9.447101818181817 | 8.440257552550214e-2 | 0.10589937675019803 | 6.67451371767209e-2 | Sitting |
An activity-based music-recommendation system - a diatribe:
For example, you may want to correlate historical music-listening habits for a user with their historical physical activities and then recommend music to them using a subsequent recommender system pipeline that uses activity prediciton as one of its input.
NOTE: One has to be legally compliant for using the data in such manner with client consent, depending on the jurisdiction of your operations - GDPR applies for EU operations, for example --- this is coming attraction!
display(same_rfModelFromSavedFile.transform(testData.sample(0.01))) // note more can be done with such data, time of day can be informative for other decision problems
- Download and Load Data
The following anonymized dataset is used with kind permission of Amira Lakhal.
wget http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
pwd && ls
//dbutils.fs.mkdirs("dbfs:///datasets/sds/ActivityRecognition") // make directory if needed
dbutils.fs.mv("file:///databricks/driver/dataTraining.csv","/datasets/sds/ActivityRecognition/dataTraining.csv")
res38: Boolean = true
display(dbutils.fs.ls("/datasets/sds/ActivityRecognition"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/sds/ActivityRecognition/dataTraining.csv | dataTraining.csv | 931349.0 |
| dbfs:/datasets/sds/ActivityRecognition/preTrainedModels/ | preTrainedModels/ | 0.0 |
dbutils.fs.head("/datasets/sds/ActivityRecognition/dataTraining.csv")
[Truncated to first 65536 bytes]
res40: String =
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
"user_001","Jumping",1446047227865,"3.44943","-1.68549","2.29862"
"user_001","Jumping",1446047227930,"1.87829","-1.91542","0.880768"
"user_001","Jumping",1446047227995,"1.57173","-5.86241","-3.75599"
"user_001","Jumping",1446047228059,"3.41111","-17.93331","0.535886"
"user_001","Jumping",1446047228123,"3.18118","-19.58108","5.74745"
"user_001","Jumping",1446047228189,"7.85626","-19.2362","0.804128"
"user_001","Jumping",1446047228253,"1.26517","-8.85139","2.18366"
"user_001","Jumping",1446047228318,"0.077239","1.15021","1.53221"
"user_001","Jumping",1446047228383,"0.230521","2.0699","-1.41845"
"user_001","Jumping",1446047228447,"0.652044","-0.497565","1.76214"
"user_001","Jumping",1446047228512,"1.53341","-0.305964","1.41725"
"user_001","Jumping",1446047228578,"-1.07237","-1.95374","0.191003"
"user_001","Jumping",1446047228642,"2.75966","-13.75639","0.191003"
"user_001","Jumping",1446047228707,"7.43474","-19.58108","2.95007"
"user_001","Jumping",1446047228771,"5.63368","-19.58108","5.59417"
"user_001","Jumping",1446047228836,"5.02056","-11.72542","-1.38013"
"user_001","Jumping",1446047228900,"-2.10702","0.575403","1.07237"
"user_001","Jumping",1446047228966,"-1.30229","2.2615","-1.26517"
"user_001","Jumping",1446047229030,"1.68669","-0.957409","1.57053"
"user_001","Jumping",1446047229095,"2.60638","-0.229323","2.14534"
"user_001","Jumping",1446047229159,"1.30349","-0.152682","0.497565"
"user_001","Jumping",1446047229224,"1.64837","-8.12331","1.60885"
"user_001","Jumping",1446047229290,"0.15388","-18.46979","-1.03525"
"user_001","Jumping",1446047229354,"4.98224","-19.58108","0.995729"
"user_001","Jumping",1446047229419,"5.17384","-19.2362","0.612526"
"user_001","Jumping",1446047229483,"2.03158","-9.11964","1.34061"
"user_001","Jumping",1446047229549,"-1.95374","-1.03405","0.574206"
"user_001","Jumping",1446047229612,"0.1922","1.30349","-1.03525"
"user_001","Jumping",1446047229678,"1.64837","-0.727487","-0.307161"
"user_001","Jumping",1446047229743,"2.56806","-1.03405","0.267643"
"user_001","Jumping",1446047229806,"1.41845","-0.305964","0.727487"
"user_001","Jumping",1446047229872,"1.03525","-6.82042","0.957409"
"user_001","Jumping",1446047229936,"3.94759","-19.31284","-1.22685"
"user_001","Jumping",1446047230001,"6.13185","-19.58108","2.72014"
"user_001","Jumping",1446047230066,"7.89458","-16.9753","0.229323"
"user_001","Jumping",1446047230131,"2.6447","-3.75479","0.114362"
"user_001","Jumping",1446047230195,"-2.41358","1.91661","-0.000599"
"user_001","Jumping",1446047230260,"-1.95374","-0.114362","-0.268841"
"user_001","Jumping",1446047230325,"-0.229323","-1.95374","2.75846"
"user_001","Jumping",1446047230390,"2.68302","0.268841","0.535886"
"user_001","Jumping",1446047230455,"1.15021","-1.41725","0.880768"
"user_001","Jumping",1446047230519,"2.98958","-11.53382","-0.920286"
"user_001","Jumping",1446047230584,"8.00954","-19.58108","-0.1922"
"user_001","Jumping",1446047230649,"7.31978","-19.58108","2.52854"
"user_001","Jumping",1446047230713,"5.44208","-13.56479","-0.498763"
"user_001","Jumping",1446047230779,"0.728685","-0.420925","1.68549"
"user_001","Jumping",1446047230842,"1.18853","1.41845","-2.22318"
"user_001","Jumping",1446047230907,"0.15388","-1.80046","2.03038"
"user_001","Jumping",1446047230972,"-0.037722","1.07357","-0.958607"
"user_001","Jumping",1446047231038,"0.230521","0.728685","-0.690364"
"user_001","Jumping",1446047231102,"0.805325","-1.14901","1.53221"
"user_001","Jumping",1446047231167,"5.17384","-9.15796","-2.91294"
"user_001","Jumping",1446047231231,"8.00954","-19.54276","1.49389"
"user_001","Jumping",1446047231296,"11.61165","-19.58108","4.25296"
"user_001","Jumping",1446047231361,"7.89458","-18.35483","3.71647"
"user_001","Jumping",1446047231426,"0.537083","-3.21831","0.420925"
"user_001","Jumping",1446047231490,"-1.91542","4.10087","-2.6447"
"user_001","Jumping",1446047231555,"-0.919089","-0.382604","0.114362"
"user_001","Jumping",1446047231620,"0.613724","-0.842448","1.57053"
"user_001","Jumping",1446047231684,"1.38013","-0.459245","0.305964"
"user_001","Jumping",1446047231749,"2.29982","-1.49389","-1.26517"
"user_001","Jumping",1446047231814,"4.94392","-10.95901","-0.498763"
"user_001","Jumping",1446047231878,"3.75599","-19.27452","-1.76333"
"user_001","Jumping",1446047231944,"7.70298","-19.58108","-2.95126"
"user_001","Jumping",1446047232008,"6.55337","-17.51178","-1.91661"
"user_001","Jumping",1446047232073,"2.10822","-2.03038","-0.268841"
"user_001","Jumping",1446047232138,"-1.68549","3.67935","-0.881966"
"user_001","Jumping",1446047232203,"0.15388","-0.114362","0.076042"
"user_001","Jumping",1446047232267,"2.79798","-1.95374","0.689167"
"user_001","Jumping",1446047232332,"1.45677","-0.957409","0.420925"
"user_001","Jumping",1446047232397,"1.18853","-2.95007","0.650847"
"user_001","Jumping",1446047232462,"3.44943","-13.87135","-3.71767"
"user_001","Jumping",1446047232526,"3.79431","-19.58108","-0.345482"
"user_001","Jumping",1446047232591,"6.09353","-19.58108","2.87342"
"user_001","Jumping",1446047232655,"4.48408","-14.3312","-0.498763"
"user_001","Jumping",1446047232720,"-0.037722","-1.68549","2.6435"
"user_001","Jumping",1446047232785,"0.613724","2.68302","-2.37646"
"user_001","Jumping",1446047232850,"0.996927","-0.497565","0.497565"
"user_001","Jumping",1446047232915,"0.15388","-0.689167","1.34061"
"user_001","Jumping",1446047232979,"1.07357","1.07357","-2.60638"
"user_001","Jumping",1446047233044,"2.37646","-3.29495","-1.45677"
"user_001","Jumping",1446047233109,"2.22318","-16.09393","1.37893"
"user_001","Jumping",1446047233173,"6.82161","-19.58108","0.037722"
"user_001","Jumping",1446047233238,"8.39275","-19.54276","1.22565"
"user_001","Jumping",1446047233303,"1.22685","-9.50284","-0.307161"
"user_001","Jumping",1446047233368,"1.83997","-0.152682","1.41725"
"user_001","Jumping",1446047233432,"-0.765807","0.996927","-1.91661"
"user_001","Jumping",1446047233497,"0.728685","-0.612526","0.152682"
"user_001","Jumping",1446047233562,"0.996927","-0.305964","0.995729"
"user_001","Jumping",1446047233626,"1.22685","-0.305964","0.037722"
"user_001","Jumping",1446047233692,"1.91661","-2.95007","-0.345482"
"user_001","Jumping",1446047233755,"4.75232","-14.63776","-3.67935"
"user_001","Jumping",1446047233821,"4.56072","-19.58108","-2.6447"
"user_001","Jumping",1446047233886,"10.69197","-19.38948","-5.94025"
"user_001","Jumping",1446047233950,"3.10454","-10.42253","-2.22318"
"user_001","Jumping",1446047234015,"-0.152682","1.61005","1.80046"
"user_001","Jumping",1446047234080,"-0.267643","1.83997","-0.958607"
"user_001","Jumping",1446047234145,"1.99325","-0.842448","-0.230521"
"user_001","Jumping",1446047234210,"2.79798","-0.114362","0.919089"
"user_001","Jumping",1446047234273,"1.11189","-0.152682","1.83878"
"user_001","Jumping",1446047234339,"2.75966","-5.05768","-0.537083"
"user_001","Jumping",1446047234403,"4.33079","-16.82202","-3.06622"
"user_001","Jumping",1446047234468,"6.89825","-19.58108","-4.25415"
"user_001","Jumping",1446047234533,"11.49669","-19.50444","-2.52974"
"user_001","Jumping",1446047234598,"6.20849","-9.08132","-1.80165"
"user_001","Jumping",1446047234663,"0.805325","1.15021","-0.15388"
"user_001","Jumping",1446047234726,"-1.76214","2.14654","-0.460442"
"user_001","Jumping",1446047234792,"0.000599","-1.11069","-0.230521"
"user_001","Jumping",1446047234857,"0.652044","0.230521","-0.920286"
"user_001","Jumping",1446047234922,"1.26517","-0.114362","-0.000599"
"user_001","Jumping",1446047234986,"5.36544","-4.09967","-0.537083"
"user_001","Jumping",1446047235051,"6.51505","-17.05194","-3.56439"
"user_001","Jumping",1446047235115,"6.93658","-19.58108","-2.75966"
"user_001","Jumping",1446047235181,"7.89458","-19.58108","-3.18118"
"user_001","Jumping",1446047235245,"1.95493","-9.6178","-1.34181"
"user_001","Jumping",1446047235310,"-2.18366","3.14286","-0.1922"
"user_001","Jumping",1446047235375,"-2.87342","1.99325","-1.76333"
"user_001","Jumping",1446047235439,"1.03525","-1.80046","1.64717"
"user_001","Jumping",1446047235504,"1.95493","-0.689167","1.49389"
"user_001","Jumping",1446047235569,"1.26517","0.383802","-0.307161"
"user_001","Jumping",1446047235633,"1.18853","-2.72014","1.41725"
"user_001","Jumping",1446047235698,"5.71033","-16.51545","-2.03158"
"user_001","Jumping",1446047235763,"6.55337","-19.58108","0.995729"
"user_001","Jumping",1446047235828,"10.577","-19.4278","-1.57173"
"user_001","Jumping",1446047235892,"2.79798","-9.88604","-0.843646"
"user_001","Jumping",1446047235957,"0.652044","-0.037722","1.14901"
"user_001","Jumping",1446047236022,"0.843646","1.95493","-2.14654"
"user_001","Jumping",1446047236086,"0.15388","-0.305964","0.995729"
"user_001","Jumping",1446047236152,"0.767005","-0.114362","0.804128"
"user_001","Jumping",1446047236215,"-0.650847","-0.650847","-0.230521"
"user_001","Jumping",1446047236281,"3.56439","-5.90073","0.727487"
"user_001","Jumping",1446047236346,"7.05154","-17.97163","-1.61005"
"user_001","Jumping",1446047236410,"9.46572","-19.58108","2.10702"
"user_001","Jumping",1446047236475,"7.58802","-18.54643","3.10335"
"user_001","Jumping",1446047236540,"0.767005","-6.28393","0.689167"
"user_001","Jumping",1446047236604,"0.307161","2.29982","-0.498763"
"user_001","Jumping",1446047236669,"0.11556","-0.114362","-1.11189"
"user_001","Jumping",1446047236734,"2.2615","-1.45557","2.14534"
"user_001","Jumping",1446047236798,"1.80165","0.345482","0.765807"
"user_001","Jumping",1446047236863,"-0.650847","-0.305964","-1.15021"
"user_001","Jumping",1446047236928,"3.48775","-7.31858","-0.728685"
"user_001","Jumping",1446047236992,"5.32712","-18.92964","0.114362"
"user_001","Jumping",1446047237057,"10.34708","-19.58108","2.37526"
"user_001","Jumping",1446047237122,"5.97857","-17.78002","0.344284"
"user_001","Jumping",1446047237187,"2.8363","-3.25663","0.152682"
"user_001","Jumping",1446047237252,"-0.995729","3.37279","-1.64837"
"user_001","Jumping",1446047237317,"-1.64717","-0.650847","1.41725"
"user_001","Jumping",1446047237381,"1.30349","-0.152682","1.45557"
"user_001","Jumping",1446047237446,"2.79798","0.000599","-0.460442"
"user_001","Jumping",1446047237510,"1.41845","-0.459245","-1.38013"
"user_001","Jumping",1446047237575,"3.90927","-6.62882","-1.49509"
"user_001","Jumping",1446047237640,"4.98224","-19.27452","1.57053"
"user_001","Jumping",1446047237705,"12.22478","-19.58108","2.0687"
"user_001","Jumping",1446047237769,"4.5224","-16.74538","-0.383802"
"user_001","Jumping",1446047237834,"0.690364","-0.880768","-0.537083"
"user_001","Jumping",1446047237899,"1.26517","2.18486","-1.03525"
"user_001","Jumping",1446047237964,"2.6447","-0.037722","1.18733"
"user_001","Jumping",1446047238028,"2.56806","-0.114362","1.03405"
"user_001","Jumping",1446047238094,"-1.14901","0.11556","-0.307161"
"user_001","Jumping",1446047238157,"-0.574206","-0.114362","-0.920286"
"user_001","Jumping",1446047238223,"2.60638","-3.40991","-0.422122"
"user_001","Jumping",1446047238287,"6.32345","-17.62674","-0.728685"
"user_001","Jumping",1446047238352,"10.53868","-19.58108","0.919089"
"user_001","Jumping",1446047238417,"7.85626","-18.20155","0.995729"
"user_001","Jumping",1446047238481,"-0.152682","-1.41725","-1.22685"
"user_001","Jumping",1446047238546,"1.26517","2.22318","-1.87829"
"user_001","Jumping",1446047238611,"0.537083","-0.497565","1.76214"
"user_001","Jumping",1446047238676,"1.95493","0.230521","1.60885"
"user_001","Jumping",1446047238741,"0.920286","0.537083","-0.422122"
"user_001","Jumping",1446047238806,"0.000599","-0.650847","0.497565"
"user_001","Jumping",1446047238870,"1.64837","-1.99206","-1.72501"
"user_001","Jumping",1446047238935,"5.0972","-14.75272","-1.41845"
"user_001","Jumping",1446047239000,"10.50036","-19.58108","1.91542"
"user_001","Jumping",1446047239064,"8.16283","-19.58108","1.95374"
"user_001","Jumping",1446047239129,"3.56439","-9.4262","-1.22685"
"user_001","Jumping",1446047239194,"0.920286","2.79798","-1.34181"
"user_001","Jumping",1446047239259,"0.1922","0.652044","-0.537083"
"user_001","Jumping",1446047239324,"1.64837","-0.957409","1.41725"
"user_001","Jumping",1446047239388,"1.99325","-0.191003","0.957409"
"user_001","Jumping",1446047239453,"1.26517","-0.305964","-0.000599"
"user_001","Jumping",1446047239518,"2.4531","-4.17632","-1.34181"
"user_001","Jumping",1446047239583,"4.44576","-16.05561","-0.537083"
"user_001","Jumping",1446047239647,"9.58068","-19.58108","1.53221"
"user_001","Jumping",1446047239712,"8.04786","-19.2362","2.0687"
"user_001","Jumping",1446047239777,"1.80165","-6.01569","-0.422122"
"user_001","Jumping",1446047239841,"0.11556","2.10822","-2.6447"
"user_001","Jumping",1446047239906,"0.345482","-0.612526","-1.22685"
"user_001","Jumping",1446047239971,"1.45677","-0.880768","2.2603"
"user_001","Jumping",1446047240036,"0.843646","1.11189","-0.537083"
"user_001","Jumping",1446047240100,"0.996927","0.038919","-0.575403"
"user_001","Jumping",1446047240166,"3.29615","-3.98471","-0.422122"
"user_001","Jumping",1446047240231,"5.21216","-16.7837","-0.613724"
"user_001","Jumping",1446047240295,"7.39642","-19.58108","1.11069"
"user_001","Jumping",1446047240360,"10.23212","-19.58108","0.459245"
"user_001","Jumping",1446047240424,"1.80165","-12.14694","-1.22685"
"user_001","Jumping",1446047240489,"0.575403","1.83997","0.037722"
"user_001","Jumping",1446047240553,"-0.152682","2.22318","-3.0279"
"user_001","Jumping",1446047240618,"0.268841","-1.91542","3.06503"
"user_001","Jumping",1446047240683,"1.72501","-0.535886","1.83878"
"user_001","Jumping",1446047240748,"-1.18733","0.996927","-1.26517"
"user_001","Jumping",1446047240813,"0.920286","-0.535886","-1.91661"
"user_001","Jumping",1446047240878,"4.40743","-11.61046","0.459245"
"user_001","Jumping",1446047240941,"6.78329","-19.58108","4.82776"
"user_001","Jumping",1446047241007,"9.35075","-19.58108","5.74745"
"user_001","Jumping",1446047241072,"5.71033","-15.74905","-1.15021"
"user_001","Jumping",1446047241136,"-1.57053","-1.80046","-0.15388"
"user_001","Jumping",1446047241201,"-0.267643","2.18486","-2.14654"
"user_001","Jumping",1446047241266,"2.22318","-1.80046","1.80046"
"user_001","Jumping",1446047241330,"-1.34061","0.767005","-2.03158"
"user_001","Jumping",1446047241395,"-0.382604","-0.842448","0.382604"
"user_001","Jumping",1446047241459,"-0.765807","-1.41725","0.076042"
"user_001","Jumping",1446047241525,"5.97857","-10.34589","-1.49509"
"user_001","Jumping",1446047241589,"11.34341","-19.58108","6.13065"
"user_001","Jumping",1446047241654,"10.15548","-19.58108","8.58315"
"user_001","Jumping",1446047241718,"4.67568","-11.61046","-0.307161"
"user_001","Jumping",1446047241783,"-1.37893","-0.650847","0.459245"
"user_001","Jumping",1446047241849,"-0.919089","2.6447","-3.14286"
"user_001","Jumping",1446047241913,"1.07357","-1.03405","-0.613724"
"user_001","Jumping",1446047241977,"0.15388","-0.919089","2.18366"
"user_001","Jumping",1446047242042,"-0.995729","0.460442","0.305964"
"user_001","Jumping",1446047242107,"2.03158","-0.382604","0.076042"
"user_001","Jumping",1446047242172,"6.93658","-10.84405","-0.422122"
"user_001","Jumping",1446047242237,"13.79591","-19.58108","1.49389"
"user_001","Jumping",1446047242302,"15.86521","-19.50444","-1.72501"
"user_001","Jumping",1446047242366,"4.79064","-12.03198","1.8771"
"user_001","Jumping",1446047242431,"-0.497565","1.45677","-4.10087"
"user_001","Jumping",1446047242495,"0.11556","0.613724","-1.30349"
"user_001","Jumping",1446047242560,"-0.459245","-0.305964","2.56686"
"user_001","Jumping",1446047242625,"2.29982","1.18853","-0.767005"
"user_001","Jumping",1446047242690,"1.38013","-0.305964","-1.87829"
"user_001","Jumping",1446047242754,"2.52974","-2.75846","0.152682"
"user_001","Jumping",1446047242819,"5.32712","-13.21991","-1.34181"
"user_001","Jumping",1446047242884,"10.50036","-19.58108","1.8771"
"user_001","Jumping",1446047242948,"13.02951","-19.58108","-0.498763"
"user_001","Jumping",1446047243013,"4.33079","-10.34589","-2.4531"
"user_001","Jumping",1446047243078,"0.613724","0.498763","2.49022"
"user_001","Jumping",1446047243143,"0.307161","1.53341","-2.49142"
"user_001","Jumping",1446047243207,"0.077239","-0.344284","1.91542"
"user_001","Jumping",1446047243272,"2.03158","0.1922","-0.000599"
"user_001","Jumping",1446047243337,"1.22685","0.230521","-1.87829"
"user_001","Jumping",1446047243401,"0.537083","-1.26397","0.535886"
"user_001","Jumping",1446047243467,"5.2888","-11.22725","0.689167"
"user_001","Jumping",1446047243531,"11.57333","-19.58108","0.344284"
"user_001","Jumping",1446047243596,"14.524","-19.58108","2.33694"
"user_001","Jumping",1446047243661,"3.18118","-11.8787","0.191003"
"user_001","Jumping",1446047243725,"-1.07237","1.15021","0.152682"
"user_001","Jumping",1446047243790,"0.422122","1.95493","-1.87829"
"user_001","Jumping",1446047243855,"-0.535886","-0.957409","1.18733"
"user_001","Jumping",1446047243920,"3.60271","-0.114362","0.574206"
"user_001","Jumping",1446047243984,"2.87462","-0.535886","-0.268841"
"user_001","Jumping",1446047244049,"2.2615","-1.53221","-0.11556"
"user_001","Jumping",1446047244114,"6.85994","-10.61413","-1.11189"
"user_001","Jumping",1446047244178,"7.97122","-19.58108","-0.077239"
"user_001","Jumping",1446047244243,"14.86888","-19.58108","-1.41845"
"user_001","Jumping",1446047244308,"8.50771","-14.48448","-0.613724"
"user_001","Jumping",1446047244373,"-0.535886","-1.18733","1.64717"
"user_001","Jumping",1446047244437,"-4.5212","3.37279","0.191003"
"user_001","Jumping",1446047244502,"-1.72382","0.690364","0.191003"
"user_003","Jumping",1446047778034,"-2.75846","-7.28026","-1.45677"
"user_003","Jumping",1446047778099,"-3.75479","-7.08866","-2.37646"
"user_003","Jumping",1446047778164,"-2.14534","-6.74378","-2.22318"
"user_003","Jumping",1446047778229,"-2.22198","-6.01569","-2.2615"
"user_003","Jumping",1446047778293,"-3.63983","-9.04299","-2.6447"
"user_003","Jumping",1446047778358,"-4.82776","-17.09026","-4.02423"
"user_003","Jumping",1446047778422,"-5.82409","-19.46612","-6.70665"
"user_003","Jumping",1446047778488,"0.805325","-10.92069","-1.49509"
"user_003","Jumping",1446047778552,"-4.67448","1.22685","1.64717"
"user_003","Jumping",1446047778616,"-1.37893","0.460442","-2.18486"
"user_003","Jumping",1446047778682,"0.1922","-0.650847","-0.077239"
"user_003","Jumping",1446047778746,"-0.842448","0.11556","-1.26517"
"user_003","Jumping",1446047778811,"-1.26397","-11.91702","-3.64103"
"user_003","Jumping",1446047778876,"-1.41725","-19.58108","-0.767005"
"user_003","Jumping",1446047778940,"-1.76214","-15.44249","-2.68302"
"user_003","Jumping",1446047779006,"-0.574206","-9.00468","-1.76333"
"user_003","Jumping",1446047779071,"-3.14167","-4.36792","-1.68669"
"user_003","Jumping",1446047779135,"-3.37159","-4.3296","-2.4531"
"user_003","Jumping",1446047779200,"-4.06135","-4.75112","-3.10454"
"user_003","Jumping",1446047779265,"-4.36792","-11.26557","-0.345482"
"user_003","Jumping",1446047779329,"-5.97737","-19.12124","-4.25415"
"user_003","Jumping",1446047779394,"-3.75479","-19.19788","-4.9056"
"user_003","Jumping",1446047779459,"-0.459245","-7.39522","-0.537083"
"user_003","Jumping",1446047779524,"-2.91174","3.10454","-2.37646"
"user_003","Jumping",1446047779589,"-1.41725","-0.995729","-0.230521"
"user_003","Jumping",1446047779653,"0.422122","-0.114362","0.344284"
"user_003","Jumping",1446047779718,"-0.574206","-0.535886","-1.18853"
"user_003","Jumping",1446047779783,"-1.72382","-13.98632","-4.10087"
"user_003","Jumping",1446047779847,"-3.14167","-19.58108","-3.48775"
"user_003","Jumping",1446047779912,"-2.68182","-13.21991","-3.18118"
"user_003","Jumping",1446047779977,"-0.267643","-8.92803","-2.10822"
"user_003","Jumping",1446047780042,"-3.17999","-3.98471","-0.728685"
"user_003","Jumping",1446047780107,"-2.91174","-4.48288","-2.18486"
"user_003","Jumping",1446047780171,"-3.37159","-5.78577","-3.87095"
"user_003","Jumping",1446047780236,"-4.02303","-9.84772","-0.383802"
"user_003","Jumping",1446047780301,"-6.47553","-17.81835","-4.13919"
"user_003","Jumping",1446047780365,"-3.86975","-19.58108","-5.40376"
"user_003","Jumping",1446047780430,"-1.99206","-14.44616","-3.64103"
"user_003","Jumping",1446047780495,"-2.29862","-1.18733","-0.307161"
"user_003","Jumping",1446047780559,"-1.8771","1.99325","-1.68669"
"user_003","Jumping",1446047780625,"0.000599","-0.344284","0.650847"
"user_003","Jumping",1446047780689,"-0.459245","-0.191003","-0.613724"
"user_003","Jumping",1446047780754,"-2.60518","-6.9737","-4.714"
"user_003","Jumping",1446047780818,"-1.41725","-19.58108","-2.56806"
"user_003","Jumping",1446047780884,"-3.40991","-17.51178","-4.59904"
"user_003","Jumping",1446047780948,"-1.95374","-10.61413","-2.91294"
"user_003","Jumping",1446047781013,"-3.06503","-6.70546","-1.76333"
"user_003","Jumping",1446047781078,"-2.6435","-7.20362","-1.87829"
"user_003","Jumping",1446047781143,"-2.60518","-7.28026","-1.64837"
"user_003","Jumping",1446047781208,"-1.72382","-6.51385","-2.0699"
"user_003","Jumping",1446047781272,"-0.689167","-5.55585","-2.37646"
"user_003","Jumping",1446047781337,"-3.67815","-8.00835","-1.99325"
"user_003","Jumping",1446047781402,"-5.63249","-13.64143","-3.44943"
"user_003","Jumping",1446047781467,"-5.97737","-18.16323","-5.59536"
"user_003","Jumping",1446047781531,"-2.03038","-19.2362","-5.25048"
"user_003","Jumping",1446047781596,"-2.14534","-8.88971","-1.18853"
"user_003","Jumping",1446047781660,"-3.06503","2.52974","-0.767005"
"user_003","Jumping",1446047781725,"0.230521","0.268841","-0.345482"
"user_003","Jumping",1446047781790,"0.15388","-0.535886","-0.690364"
"user_003","Jumping",1446047781855,"-0.420925","-2.18366","-2.68302"
"user_003","Jumping",1446047781919,"-3.63983","-17.47346","-4.44576"
"user_003","Jumping",1446047781985,"-2.91174","-18.35483","-4.714"
"user_003","Jumping",1446047782049,"-0.919089","-10.88237","-2.2615"
"user_003","Jumping",1446047782114,"-3.02671","-6.93538","-2.68302"
"user_003","Jumping",1446047782178,"-2.75846","-7.3569","-2.10822"
"user_003","Jumping",1446047782244,"-1.45557","-7.85507","-1.22685"
"user_003","Jumping",1446047782309,"-1.8771","-6.32225","-1.99325"
"user_003","Jumping",1446047782373,"-1.64717","-5.90073","-2.33814"
"user_003","Jumping",1446047782438,"-2.49022","-8.08499","-1.80165"
"user_003","Jumping",1446047782503,"-4.40624","-11.26557","-2.37646"
"user_003","Jumping",1446047782568,"-5.13432","-17.1669","-4.67568"
"user_003","Jumping",1446047782632,"-3.40991","-19.54276","-5.71033"
"user_003","Jumping",1446047782697,"-1.99206","-13.48815","-4.06255"
"user_003","Jumping",1446047782762,"-2.22198","-1.68549","0.267643"
"user_003","Jumping",1446047782827,"-1.91542","1.80165","-1.68669"
"user_003","Jumping",1446047782891,"0.575403","-0.152682","0.459245"
"user_003","Jumping",1446047782956,"-0.842448","-0.804128","-1.03525"
"user_003","Jumping",1446047783021,"-1.76214","-12.22358","-5.67201"
"user_003","Jumping",1446047783086,"-2.33694","-19.19788","-4.02423"
"user_003","Jumping",1446047783151,"-1.14901","-14.98264","-4.13919"
"user_003","Jumping",1446047783215,"-2.22198","-9.04299","-2.91294"
"user_003","Jumping",1446047783280,"-2.41358","-5.59417","-0.613724"
"user_003","Jumping",1446047783344,"-2.60518","-5.78577","-2.33814"
"user_003","Jumping",1446047783409,"-3.67815","-4.44456","-2.4531"
"user_003","Jumping",1446047783474,"-2.8351","-4.63616","-3.75599"
"user_003","Jumping",1446047783539,"-1.64717","-16.4005","-0.843646"
"user_003","Jumping",1446047783604,"-6.62882","-19.58108","-8.58435"
"user_003","Jumping",1446047783669,"-1.72382","-14.67608","-3.52607"
"user_003","Jumping",1446047783733,"-1.57053","1.34181","0.612526"
"user_003","Jumping",1446047783798,"-1.18733","1.22685","-1.38013"
"user_003","Jumping",1446047783863,"-0.612526","-0.612526","-0.268841"
"user_003","Jumping",1446047783928,"-1.22565","0.345482","-0.805325"
"user_003","Jumping",1446047783992,"-1.11069","-2.2603","-2.56806"
"user_003","Jumping",1446047784057,"-2.8351","-17.62674","-5.02056"
"user_003","Jumping",1446047784122,"-3.90807","-19.19788","-5.59536"
"user_003","Jumping",1446047784187,"-1.72382","-12.6451","-3.64103"
"user_003","Jumping",1446047784252,"-1.49389","-6.7821","-2.0699"
"user_003","Jumping",1446047784316,"-2.14534","-4.67448","-1.15021"
"user_003","Jumping",1446047784381,"-2.91174","-6.93538","-2.6447"
"user_003","Jumping",1446047784446,"-3.71647","-5.86241","-2.56806"
"user_003","Jumping",1446047784510,"-0.344284","-6.51385","-2.8363"
"user_003","Jumping",1446047784575,"-4.02303","-14.98264","-2.2615"
"user_003","Jumping",1446047784639,"-5.36425","-19.58108","-8.89091"
"user_003","Jumping",1446047784704,"-0.191003","-14.906","-3.94759"
"user_003","Jumping",1446047784769,"-2.4519","0.077239","1.11069"
"user_003","Jumping",1446047784834,"-2.4519","1.22685","-1.41845"
"user_003","Jumping",1446047784899,"-0.344284","0.345482","-0.15388"
"user_003","Jumping",1446047784964,"-0.842448","0.422122","-0.996927"
"user_003","Jumping",1446047785029,"-2.29862","-4.3296","-4.63736"
"user_003","Jumping",1446047785093,"-0.765807","-18.92964","-3.98591"
"user_003","Jumping",1446047785158,"-4.3296","-19.38948","-5.17384"
"user_003","Jumping",1446047785222,"-1.11069","-12.6451","-1.22685"
"user_003","Jumping",1446047785287,"-3.44823","-7.24194","-2.18486"
"user_003","Jumping",1446047785352,"-2.49022","-6.51385","-2.95126"
"user_003","Jumping",1446047785417,"-2.10702","-6.85874","-1.22685"
"user_003","Jumping",1446047785482,"-2.79678","-7.05034","-2.14654"
"user_003","Jumping",1446047785546,"-2.56686","-5.01936","-2.52974"
"user_003","Jumping",1446047785611,"-2.87342","-6.62882","-2.56806"
"user_003","Jumping",1446047785676,"-4.63616","-14.44616","-2.52974"
"user_003","Jumping",1446047785741,"-5.67081","-19.58108","-6.59169"
"user_003","Jumping",1446047785805,"-3.67815","-17.66507","-6.63001"
"user_003","Jumping",1446047785870,"-0.919089","-4.82776","-0.268841"
"user_003","Jumping",1446047785934,"-2.91174","2.87462","-2.33814"
"user_003","Jumping",1446047785999,"-0.420925","-0.420925","0.919089"
"user_003","Jumping",1446047786065,"-0.037722","0.230521","0.382604"
"user_003","Jumping",1446047786129,"-0.842448","-0.497565","-3.0279"
"user_003","Jumping",1446047786194,"-0.995729","-15.48081","-4.33079"
"user_003","Jumping",1446047786259,"-3.98471","-19.50444","-6.89825"
"user_003","Jumping",1446047786324,"-0.574206","-13.29655","-3.94759"
"user_003","Jumping",1446047786388,"-1.41725","-8.85139","-1.38013"
"user_003","Jumping",1446047786453,"-3.94639","-5.59417","-3.14286"
"user_003","Jumping",1446047786518,"-2.18366","-8.04667","-3.06622"
"user_003","Jumping",1446047786582,"-1.60885","-8.92803","-1.15021"
"user_003","Jumping",1446047786647,"-2.72014","-5.13432","-2.33814"
"user_003","Jumping",1446047786712,"-2.03038","-4.25296","-3.18118"
"user_003","Jumping",1446047786777,"-3.48655","-9.84772","-1.45677"
"user_003","Jumping",1446047786841,"-6.01569","-18.96796","-6.78329"
"user_003","Jumping",1446047786906,"-4.44456","-19.4278","-8.62267"
"user_003","Jumping",1446047786970,"-0.267643","-8.92803","-1.57173"
"user_003","Jumping",1446047787035,"-2.91174","3.41111","-1.53341"
"user_003","Jumping",1446047787100,"0.268841","-0.765807","0.689167"
"user_003","Jumping",1446047787165,"-0.650847","0.383802","-0.728685"
"user_003","Jumping",1446047787230,"-0.880768","0.422122","-1.03525"
"user_003","Jumping",1446047787294,"-1.60885","-11.11229","-5.4804"
"user_003","Jumping",1446047787358,"-4.59784","-19.35116","-5.94025"
"user_003","Jumping",1446047787424,"-2.41358","-15.71073","-5.74865"
"user_003","Jumping",1446047787489,"-1.26397","-9.34956","-3.06622"
"user_003","Jumping",1446047787553,"-3.44823","-6.47553","-2.75966"
"user_003","Jumping",1446047787618,"-2.4519","-7.97003","-2.2615"
"user_003","Jumping",1446047787683,"-1.95374","-7.81675","-1.83997"
"user_003","Jumping",1446047787747,"-2.75846","-4.67448","-3.06622"
"user_003","Jumping",1446047787812,"-2.22198","-5.24928","-2.0699"
"user_003","Jumping",1446047787877,"-3.29495","-12.41518","-1.49509"
"user_003","Jumping",1446047787942,"-5.44089","-19.46612","-6.20849"
"user_003","Jumping",1446047788007,"-3.79311","-18.69972","-7.70298"
"user_003","Jumping",1446047788071,"-0.382604","-7.1653","-1.83997"
"user_003","Jumping",1446047788136,"-3.14167","2.72134","-0.575403"
"user_003","Jumping",1446047788201,"0.460442","-0.152682","0.114362"
"user_003","Jumping",1446047788265,"-0.842448","0.652044","-0.230521"
"user_003","Jumping",1446047788331,"-2.2603","-0.229323","-1.45677"
"user_003","Jumping",1446047788395,"-1.45557","-13.71807","-6.63001"
"user_003","Jumping",1446047788460,"-3.14167","-19.54276","-6.32345"
"user_003","Jumping",1446047788524,"-1.83878","-13.06663","-4.40743"
"user_003","Jumping",1446047788590,"-1.83878","-9.77108","-3.25783"
"user_003","Jumping",1446047788654,"-3.14167","-6.55217","-2.6447"
"user_003","Jumping",1446047788719,"-2.10702","-7.31858","-2.10822"
"user_003","Jumping",1446047788784,"-2.14534","-5.90073","-2.0699"
"user_003","Jumping",1446047788848,"-1.57053","-4.9044","-2.75966"
"user_003","Jumping",1446047788913,"-2.75846","-7.5485","-1.76333"
"user_003","Jumping",1446047788978,"-4.98104","-15.97897","-4.67568"
"user_003","Jumping",1446047789043,"-3.10335","-19.58108","-8.16283"
"user_003","Jumping",1446047789107,"-1.68549","-16.36218","-5.25048"
"user_003","Jumping",1446047789172,"-2.52854","-2.0687","-0.345482"
"user_003","Jumping",1446047789236,"-1.72382","2.41478","-2.37646"
"user_003","Jumping",1446047789301,"0.843646","-0.382604","1.11069"
"user_003","Jumping",1446047789366,"-0.689167","0.230521","-0.728685"
"user_003","Jumping",1446047789431,"-1.14901","-1.95374","-2.72134"
"user_003","Jumping",1446047789495,"-3.90807","-17.74171","-7.58802"
"user_003","Jumping",1446047789560,"-3.29495","-18.96796","-5.05888"
"user_003","Jumping",1446047789625,"-2.22198","-12.0703","-3.98591"
"user_003","Jumping",1446047789690,"-2.79678","-7.7401","-2.37646"
"user_003","Jumping",1446047789755,"-1.95374","-5.74745","-2.22318"
"user_003","Jumping",1446047789818,"-2.03038","-6.85874","-1.68669"
"user_003","Jumping",1446047789883,"-2.91174","-5.67081","-2.52974"
"user_003","Jumping",1446047789949,"-1.49389","-4.94272","-2.75966"
"user_003","Jumping",1446047790013,"-3.37159","-10.88237","-1.57173"
"user_003","Jumping",1446047790078,"-5.51753","-19.58108","-7.24314"
"user_003","Jumping",1446047790143,"-4.48288","-17.5501","-6.9749"
"user_003","Jumping",1446047790207,"-0.612526","-2.68182","-0.613724"
"user_003","Jumping",1446047790272,"-1.8771","2.68302","-2.03158"
"user_003","Jumping",1446047790337,"1.15021","-0.919089","1.64717"
"user_003","Jumping",1446047790402,"-2.03038","0.307161","-1.22685"
"user_003","Jumping",1446047790467,"-1.34061","-0.191003","-1.61005"
"user_003","Jumping",1446047790531,"-3.02671","-13.64143","-5.17384"
"user_003","Jumping",1446047790596,"-4.75112","-19.58108","-7.62634"
"user_003","Jumping",1446047790660,"-2.60518","-15.05928","-6.01689"
"user_003","Jumping",1446047790726,"-1.26397","-8.12331","-3.10454"
"user_003","Jumping",1446047790790,"-3.25663","-5.97737","-3.0279"
"user_003","Jumping",1446047790855,"-0.420925","-6.24561","-2.2615"
"user_003","Jumping",1446047790919,"-1.83878","-3.40991","-3.60271"
"user_003","Jumping",1446047790984,"-3.94639","-5.36425","-3.75599"
"user_003","Jumping",1446047791049,"-2.2603","-13.98632","-3.67935"
"user_003","Jumping",1446047791114,"-7.70178","-19.58108","-7.5497"
"user_003","Jumping",1446047791179,"-2.52854","-15.36585","-6.66833"
"user_003","Jumping",1446047791243,"-0.574206","-0.497565","-0.230521"
"user_003","Jumping",1446047791308,"-2.0687","0.958607","-0.575403"
"user_003","Jumping",1446047791373,"0.11556","-0.497565","0.344284"
"user_003","Jumping",1446047791438,"-1.30229","1.34181","-1.11189"
"user_003","Jumping",1446047791503,"-0.382604","-0.880768","-2.22318"
"user_003","Jumping",1446047791568,"-2.87342","-15.97897","-6.28513"
"user_003","Jumping",1446047791632,"-2.95007","-19.38948","-7.51138"
"user_003","Jumping",1446047791697,"-4.59784","-13.37319","-5.02056"
"user_003","Jumping",1446047791762,"-2.14534","-9.84772","-3.2195"
"user_003","Jumping",1446047791827,"-2.29862","-6.55217","-2.49142"
"user_003","Jumping",1446047791891,"-1.68549","-7.47186","-2.29982"
"user_003","Jumping",1446047791956,"-1.22565","-8.16163","-2.56806"
"user_003","Jumping",1446047792021,"-2.95007","-5.096","-2.52974"
"user_003","Jumping",1446047792085,"-4.02303","-4.06135","-7.7413"
"user_003","Jumping",1446047792151,"-2.60518","-14.40784","0.727487"
"user_003","Jumping",1446047792215,"-6.24561","-19.58108","-8.89091"
"user_003","Jumping",1446047792280,"-1.60885","-12.56846","-3.2195"
"user_003","Jumping",1446047792344,"-3.10335","3.14286","-1.26517"
"user_003","Jumping",1446047792410,"-0.037722","0.000599","-0.11556"
"user_003","Jumping",1446047792474,"0.268841","-0.650847","0.114362"
"user_003","Jumping",1446047792539,"-2.68182","0.996927","-1.99325"
"user_003","Jumping",1446047792603,"-1.64717","-4.67448","-5.51872"
"user_003","Jumping",1446047792669,"-2.87342","-18.96796","-5.86361"
"user_003","Jumping",1446047792733,"-3.29495","-18.66139","-4.44576"
"user_003","Jumping",1446047792798,"-4.02303","-13.21991","-4.79064"
"user_003","Jumping",1446047792863,"-2.95007","-7.97003","-2.79798"
"user_003","Jumping",1446047792928,"-3.17999","-6.20729","-2.29982"
"user_003","Jumping",1446047792992,"-3.25663","-5.40257","-2.37646"
"user_003","Jumping",1446047793057,"-3.40991","-4.5212","-3.18118"
"user_003","Jumping",1446047793122,"-3.29495","-3.90807","-3.94759"
"user_003","Jumping",1446047793186,"-4.7128","-14.7144","-2.75966"
"user_003","Jumping",1446047793251,"-4.55952","-19.58108","-7.77962"
"user_003","Jumping",1446047793316,"-2.95007","-16.32385","-6.93658"
"user_003","Jumping",1446047793380,"-1.26397","-2.14534","-1.38013"
"user_003","Jumping",1446047793445,"-2.10702","2.33814","-1.64837"
"user_003","Jumping",1446047793510,"-0.420925","-0.267643","1.07237"
"user_003","Jumping",1446047793575,"-0.995729","0.383802","-0.537083"
"user_003","Jumping",1446047793640,"-1.14901","-1.76214","-2.29982"
"user_003","Jumping",1446047793704,"-2.56686","-14.82936","-6.28513"
"user_003","Jumping",1446047793768,"-3.40991","-19.31284","-7.3581"
"user_003","Jumping",1446047793834,"-3.52487","-13.87135","-5.63368"
"user_003","Jumping",1446047793899,"-2.68182","-10.84405","-3.10454"
"user_003","Jumping",1446047793963,"-2.72014","-4.78944","-2.72134"
"user_003","Jumping",1446047794028,"-1.03405","-4.94272","-1.68669"
"user_003","Jumping",1446047794093,"-2.10702","-5.93905","-2.14654"
"user_003","Jumping",1446047794158,"-1.57053","-5.90073","-3.2195"
"user_003","Jumping",1446047794222,"-4.3296","-8.08499","-1.15021"
"user_003","Jumping",1446047794287,"-4.48288","-19.08292","-6.36177"
"user_003","Jumping",1446047794352,"-4.67448","-19.58108","-8.27779"
"user_003","Jumping",1446047794416,"0.000599","-10.76741","-2.33814"
"user_003","Jumping",1446047794481,"-1.95374","3.41111","-1.07357"
"user_003","Jumping",1446047794546,"-0.152682","-0.689167","0.229323"
"user_003","Jumping",1446047794611,"-0.957409","0.422122","-0.460442"
"user_003","Jumping",1446047794676,"-0.957409","0.996927","-0.881966"
"user_003","Jumping",1446047794740,"-1.57053","-3.60151","-4.75232"
"user_003","Jumping",1446047794805,"-1.99206","-18.35483","-5.78697"
"user_003","Jumping",1446047794870,"-3.71647","-19.2362","-6.47673"
"user_003","Jumping",1446047794935,"-0.842448","-14.94432","-4.17751"
"user_002","Standing",1446309439342,"-2.49022","-9.19628","-1.11189"
"user_002","Standing",1446309439349,"-2.41358","-9.15796","-1.22685"
"user_002","Standing",1446309439358,"-2.56686","-9.15796","-1.30349"
"user_002","Standing",1446309439365,"-2.75846","-9.08132","-1.11189"
"user_002","Standing",1446309439373,"-2.75846","-9.08132","-1.15021"
"user_002","Standing",1446309439382,"-2.8351","-9.15796","-0.920286"
"user_002","Standing",1446309439390,"-2.8357","-9.15855","-0.919687"
"user_002","Standing",1446309439398,"-2.95007","-9.2346","-0.881966"
"user_002","Standing",1446309439406,"-2.8351","-9.11964","-0.958607"
"user_002","Standing",1446309439414,"-2.8351","-8.96635","-1.15021"
"user_002","Standing",1446309439422,"-2.95007","-9.08132","-0.920286"
"user_002","Standing",1446309439430,"-2.95007","-8.88971","-0.996927"
"user_002","Standing",1446309439438,"-2.91174","-9.00468","-1.15021"
"user_002","Standing",1446309439447,"-3.02671","-8.96635","-0.996927"
"user_002","Standing",1446309439454,"-2.79678","-8.96635","-1.15021"
"user_002","Standing",1446309439463,"-2.87342","-9.08132","-1.34181"
"user_002","Standing",1446309439471,"-2.98839","-9.19628","-1.22685"
"user_002","Standing",1446309439479,"-2.95007","-9.00468","-1.30349"
"user_002","Standing",1446309439487,"-2.98839","-9.00468","-1.15021"
"user_002","Standing",1446309439495,"-3.06503","-9.00468","-0.958607"
"user_002","Standing",1446309439503,"-3.02671","-9.04299","-0.881966"
"user_002","Standing",1446309439511,"-2.91174","-9.00468","-0.920286"
"user_002","Standing",1446309439519,"-2.95007","-9.08132","-0.767005"
"user_002","Standing",1446309439527,"-2.8351","-9.00468","-0.958607"
"user_002","Standing",1446309439536,"-2.95007","-9.00468","-0.996927"
"user_002","Standing",1446309439543,"-2.75846","-9.04299","-0.690364"
"user_002","Standing",1446309439552,"-2.6435","-9.00468","-1.03525"
"user_002","Standing",1446309439560,"-2.72014","-9.00468","-1.15021"
"user_002","Standing",1446309439568,"-2.68182","-9.04299","-1.11189"
"user_002","Standing",1446309439576,"-2.60518","-9.00468","-1.11189"
"user_002","Standing",1446309439584,"-2.52854","-8.88971","-1.15021"
"user_002","Standing",1446309439593,"-2.6435","-8.92803","-1.03525"
"user_002","Standing",1446309439601,"-2.60518","-8.96635","-0.996927"
"user_002","Standing",1446309439609,"-2.6435","-9.04299","-0.996927"
"user_002","Standing",1446309439616,"-2.75846","-9.04299","-0.920286"
"user_002","Standing",1446309439624,"-2.6435","-9.19628","-0.805325"
"user_002","Standing",1446309439633,"-2.79678","-8.96635","-0.767005"
"user_002","Standing",1446309439641,"-2.87342","-8.92803","-0.958607"
"user_002","Standing",1446309439649,"-2.60518","-9.11964","-0.843646"
"user_002","Standing",1446309439657,"-2.8351","-9.08132","-0.843646"
"user_002","Standing",1446309439665,"-2.8351","-9.2346","-0.920286"
"user_002","Standing",1446309439674,"-2.72014","-9.11964","-0.958607"
"user_002","Standing",1446309439682,"-2.87342","-9.15796","-1.18853"
"user_002","Standing",1446309439690,"-3.06503","-9.2346","-1.18853"
"user_002","Standing",1446309439698,"-2.8351","-9.08132","-0.996927"
"user_002","Standing",1446309439705,"-2.98839","-9.04299","-1.03525"
"user_002","Standing",1446309439713,"-3.17999","-8.96635","-1.18853"
"user_002","Standing",1446309439722,"-3.21831","-8.88971","-1.18853"
"user_002","Standing",1446309439729,"-3.17999","-8.92803","-1.15021"
"user_002","Standing",1446309439738,"-3.17999","-8.77475","-1.22685"
"user_002","Standing",1446309439746,"-3.33327","-8.65979","-1.07357"
"user_002","Standing",1446309439754,"-3.37159","-8.58315","-1.34181"
"user_002","Standing",1446309439762,"-3.33327","-8.77475","-1.18853"
"user_002","Standing",1446309439771,"-3.33327","-8.58315","-1.07357"
"user_002","Standing",1446309439779,"-3.14167","-8.92803","-1.03525"
"user_002","Standing",1446309439787,"-3.25663","-8.85139","-0.881966"
"user_002","Standing",1446309439795,"-3.10335","-8.85139","-0.920286"
"user_002","Standing",1446309439803,"-2.87342","-8.85139","-0.843646"
"user_002","Standing",1446309439811,"-2.91174","-8.92803","-0.767005"
"user_002","Standing",1446309439819,"-2.87342","-8.88971","-0.805325"
"user_002","Standing",1446309439827,"-2.98839","-8.96635","-0.767005"
"user_002","Standing",1446309439835,"-2.79678","-9.11964","-1.07357"
"user_002","Standing",1446309439843,"-2.87342","-9.08132","-0.920286"
"user_002","Standing",1446309439851,"-2.95007","-9.15796","-0.805325"
"user_002","Standing",1446309439859,"-3.02671","-8.85139","-0.728685"
"user_002","Standing",1446309439868,"-2.98839","-8.92803","-0.690364"
"user_002","Standing",1446309439875,"-2.98839","-8.96635","-0.690364"
"user_002","Standing",1446309439883,"-2.91174","-8.96635","-0.881966"
"user_002","Standing",1446309439892,"-3.02671","-8.85139","-0.613724"
"user_002","Standing",1446309439900,"-3.06503","-9.04299","-0.767005"
"user_002","Standing",1446309439908,"-2.87342","-8.73643","-0.652044"
"user_002","Standing",1446309439916,"-2.95007","-8.88971","-0.728685"
"user_002","Standing",1446309439924,"-3.10335","-8.77475","-0.690364"
"user_002","Standing",1446309439932,"-2.91174","-8.77475","-0.652044"
"user_002","Standing",1446309439940,"-2.91174","-9.04299","-0.805325"
"user_002","Standing",1446309439948,"-2.91174","-9.04299","-0.767005"
"user_002","Standing",1446309439957,"-3.02671","-9.04299","-0.652044"
"user_002","Standing",1446309439965,"-3.06503","-9.27292","-0.805325"
"user_002","Standing",1446309439973,"-3.14167","-9.00468","-0.767005"
"user_002","Standing",1446309439980,"-2.95007","-8.96635","-0.843646"
"user_002","Standing",1446309439989,"-3.10335","-9.00468","-1.15021"
"user_002","Standing",1446309439997,"-2.98839","-9.19628","-1.03525"
"user_002","Standing",1446309440004,"-3.10335","-9.19628","-1.15021"
"user_002","Standing",1446309440013,"-2.91174","-9.11964","-1.18853"
"user_002","Standing",1446309440021,"-2.91174","-9.08132","-1.22685"
"user_002","Standing",1446309440029,"-2.95007","-9.00468","-1.26517"
"user_002","Standing",1446309440038,"-3.06503","-9.08132","-1.15021"
"user_002","Standing",1446309440045,"-3.06503","-9.00468","-1.03525"
"user_002","Standing",1446309440054,"-3.10335","-9.11964","-1.07357"
"user_002","Standing",1446309440062,"-3.10335","-8.96635","-0.996927"
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"user_002","S...
Distributed Vertex Programming using GraphX
This is an augmentation of http://go.databricks.com/hubfs/notebooks/3-GraphFrames-User-Guide-scala.html that was last retrieved in 2019.
See:
- https://amplab.cs.berkeley.edu/wp-content/uploads/2014/09/graphx.pdf
- https://amplab.github.io/graphx/
- https://spark.apache.org/docs/latest/graphx-programming-guide.html
- https://databricks.com/blog/2016/03/03/introducing-graphframes.html
- https://databricks.com/blog/2016/03/16/on-time-flight-performance-with-spark-graphframes.html
- http://ampcamp.berkeley.edu/big-data-mini-course/graph-analytics-with-graphx.html
And of course the databricks guide: * https://docs.databricks.com/spark/latest/graph-analysis/index.html
Use the source, Luke/Lea!
Community Packages in Spark - more generally
Let us recall the following quoate in Chapter 10 of High Performance Spark book (needs access to Orielly publishers via your library/subscription): - https://learning.oreilly.com/library/view/high-performance-spark/9781491943199/ch10.html#components
Beyond the integrated components, the community packages can add important functionality to Spark, sometimes even superseding built-in functionality—like with GraphFrames.
Here we introduce you to GraphFrames quickly so you don't need to drop down to the GraphX library that requires more understanding of caching and checkpointing to keep the vertex program's DAG from exploding or becoming inefficient.
GraphFrames User Guide (Scala)
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs. It provides high-level APIs in Scala, Java, and Python. It aims to provide both the functionality of GraphX and extended functionality taking advantage of Spark DataFrames. This extended functionality includes motif finding, DataFrame-based serialization, and highly expressive graph queries.
The GraphFrames package is available from Spark Packages.
This notebook demonstrates examples from the GraphFrames User Guide: https://graphframes.github.io/graphframes/docs/_site/user-guide.html.
sc.version // link the right library depending on Spark version of the cluster that's running
// spark version 2.3.0 works with graphframes:graphframes:0.7.0-spark2.3-s_2.11
// spark version 3.0.1 works with graphframes:graphframes:0.8.1-spark3.0-s_2.12
res0: String = 3.1.2
Since databricks.com stopped allowing IFrame embeds we have to open it in a separate window now. The blog is insightful and worth a perusal:
- https://databricks.com/blog/2016/03/03/introducing-graphframes.html
// we first need to install the library - graphframes as a Spark package - and attach it to our cluster - see note two cells above!
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
Creating GraphFrames
Let us try to create an example social network from the blog: * https://databricks.com/blog/2016/03/03/introducing-graphframes.html.

Users can create GraphFrames from vertex and edge DataFrames.
- Vertex DataFrame: A vertex DataFrame should contain a special column named
idwhich specifies unique IDs for each vertex in the graph. - Edge DataFrame: An edge DataFrame should contain two special columns:
src(source vertex ID of edge) anddst(destination vertex ID of edge).
Both DataFrames can have arbitrary other columns. Those columns can represent vertex and edge attributes.
In our example, we can use a GraphFrame can store data or properties associated with each vertex and edge.
In our social network, each user might have an age and name, and each connection might have a relationship type.
Create the vertices and edges
// Vertex DataFrame
val v = sqlContext.createDataFrame(List(
("a", "Alice", 34),
("b", "Bob", 36),
("c", "Charlie", 30),
("d", "David", 29),
("e", "Esther", 32),
("f", "Fanny", 36),
("g", "Gabby", 60)
)).toDF("id", "name", "age")
// Edge DataFrame
val e = sqlContext.createDataFrame(List(
("a", "b", "friend"),
("b", "c", "follow"),
("c", "b", "follow"),
("f", "c", "follow"),
("e", "f", "follow"),
("e", "d", "friend"),
("d", "a", "friend"),
("a", "e", "friend")
)).toDF("src", "dst", "relationship")
v: org.apache.spark.sql.DataFrame = [id: string, name: string ... 1 more field]
e: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
Let's create a graph from these vertices and these edges:
val g = GraphFrame(v, e)
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
Let's use the d3.graphs to visualise graphs (recall the D3 graphs in wiki-click example). You need the Run Cell below using that cell's Play button's drop-down menu.
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3.graphs.help()
Produces a force-directed graph given a collection of edges of the following form: case class Edge(src: String, dest: String, count: Long)
Usage:
import d3._
graphs.force(
height = 500,
width = 500,
clicks: Dataset[Edge])
import org.apache.spark.sql.functions.lit // import the lit function in sql
val gE= g.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")) // for us the column count is just an edge incidence
import org.apache.spark.sql.functions.lit
gE: org.apache.spark.sql.DataFrame = [src: string, dest: string ... 1 more field]
display(gE)
| src | dest | count |
|---|---|---|
| a | b | 1.0 |
| b | c | 1.0 |
| c | b | 1.0 |
| f | c | 1.0 |
| e | f | 1.0 |
| e | d | 1.0 |
| d | a | 1.0 |
| a | e | 1.0 |
d3.graphs.force(
height = 500,
width = 500,
clicks = gE.as[d3.Edge])
// This example graph also comes with the GraphFrames package.
val g0 = examples.Graphs.friends
g0: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
d3.graphs.force( // let us see g0 now in one cell
height = 500,
width = 500,
clicks = g0.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])
Basic graph and DataFrame queries
GraphFrames provide several simple graph queries, such as node degree.
Also, since GraphFrames represent graphs as pairs of vertex and edge DataFrames, it is easy to make powerful queries directly on the vertex and edge DataFrames. Those DataFrames are made available as vertices and edges fields in the GraphFrame.
Simple queries are simple
GraphFrames make it easy to express queries over graphs. Since GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries.
display(g.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g0.vertices) // this is the same query on the graph loaded as an example from GraphFrame package
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g.edges)
| src | dst | relationship |
|---|---|---|
| a | b | friend |
| b | c | follow |
| c | b | follow |
| f | c | follow |
| e | f | follow |
| e | d | friend |
| d | a | friend |
| a | e | friend |
The incoming degree of the vertices:
display(g.inDegrees)
| id | inDegree |
|---|---|
| c | 2.0 |
| d | 1.0 |
| e | 1.0 |
| b | 2.0 |
| f | 1.0 |
| a | 1.0 |
The outgoing degree of the vertices:
display(g.outDegrees)
| id | outDegree |
|---|---|
| a | 2.0 |
| c | 1.0 |
| e | 2.0 |
| d | 1.0 |
| b | 1.0 |
| f | 1.0 |
The degree of the vertices:
display(g.degrees)
| id | degree |
|---|---|
| b | 3.0 |
| a | 3.0 |
| c | 3.0 |
| f | 2.0 |
| e | 3.0 |
| d | 2.0 |
You can run queries directly on the vertices DataFrame. For example, we can find the age of the youngest person in the graph:
val youngest = g.vertices.groupBy().min("age")
display(youngest)
| min(age) |
|---|
| 29.0 |
Likewise, you can run queries on the edges DataFrame.
For example, let us count the number of 'follow' relationships in the graph:
val numFollows = g.edges.filter("relationship = 'follow'").count()
numFollows: Long = 4
Motif finding
More complex relationships involving edges and vertices can be built using motifs.
The following cell finds the pairs of vertices with edges in both directions between them.
The result is a dataframe, in which the column names are given by the motif keys.
Check out the GraphFrame User Guide at https://graphframes.github.io/graphframes/docs/_site/user-guide.html for more details on the API.
// Search for pairs of vertices with edges in both directions between them, i.e., find undirected or bidirected edges.
val motifs = g.find("(a)-[e1]->(b); (b)-[e2]->(a)")
display(motifs)
Since the result is a DataFrame, more complex queries can be built on top of the motif.
Let us find all the reciprocal relationships in which one person is older than 30:
val filtered = motifs.filter("b.age > 30")
display(filtered)
You Try!
//Search for all "directed triangles" or triplets of vertices: a,b,c with edges: a->b, b->c and c->a
//uncomment the next 2 lines and replace the "XXX" below
//val motifs3 = g.find("(a)-[e1]->(b); (b)-[e2]->(c); (c)-[e3]->(XXX)")
//display(motifs3)
Stateful queries
Many motif queries are stateless and simple to express, as in the examples above. The next examples demonstrate more complex queries which carry state along a path in the motif. These queries can be expressed by combining GraphFrame motif finding with filters on the result, where the filters use sequence operations to construct a series of DataFrame Columns.
For example, suppose one wishes to identify a chain of 4 vertices with some property defined by a sequence of functions. That is, among chains of 4 vertices a->b->c->d, identify the subset of chains matching this complex filter:
- Initialize state on path.
- Update state based on vertex a.
- Update state based on vertex b.
- Etc. for c and d.
- If final state matches some condition, then the chain is accepted by the filter.
The below code snippets demonstrate this process, where we identify chains of 4 vertices such that at least 2 of the 3 edges are friend relationships. In this example, the state is the current count of friend edges; in general, it could be any DataFrame Column.
// Find chains of 4 vertices.
val chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
// Query on sequence, with state (cnt)
// (a) Define method for updating state given the next element of the motif.
def sumFriends(cnt: Column, relationship: Column): Column = {
when(relationship === "friend", cnt + 1).otherwise(cnt)
}
// (b) Use sequence operation to apply method to sequence of elements in motif.
// In this case, the elements are the 3 edges.
val condition = Seq("ab", "bc", "cd").
foldLeft(lit(0))((cnt, e) => sumFriends(cnt, col(e)("relationship")))
// (c) Apply filter to DataFrame.
val chainWith2Friends2 = chain4.where(condition >= 2)
display(chainWith2Friends2)
chain4
res22: org.apache.spark.sql.DataFrame = [a: struct<id: string, name: string ... 1 more field>, ab: struct<src: string, dst: string ... 1 more field> ... 5 more fields]
chain4.printSchema
root
|-- a: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- ab: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- b: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- bc: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- c: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- cd: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- d: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
An idea -- a diatribe into an AI security product.
Can you think of a way to use stateful queries in social media networks to find perpetrators of hate-speech online who are possibly worthy of an investigation by domain experts, say in the intelligence or security domain, for potential prosecution on charges of having incited another person to cause physical violence... This is a real problem today as Swedish law effectively prohibits certain forms of online hate-speech.
An idea for a product that can be used by Swedish security agencies?
See https://näthatsgranskaren.se/ for details of a non-profit in Sweden doing such operaitons mostly manually as of early 2020.
Subgraphs
Subgraphs are built by filtering a subset of edges and vertices. For example, the following subgraph only contains people who are friends and who are more than 30 years old.
// Select subgraph of users older than 30, and edges of type "friend"
val v2 = g.vertices.filter("age > 30")
val e2 = g.edges.filter("relationship = 'friend'")
val g2 = GraphFrame(v2, e2)
v2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, name: string ... 1 more field]
e2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g2.edges)
| src | dst | relationship |
|---|---|---|
| a | b | friend |
| e | d | friend |
| d | a | friend |
| a | e | friend |
d3.graphs.force( // let us see g2 now in one cell
height = 500,
width = 500,
clicks = g2.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])
Complex triplet filters
The following example shows how to select a subgraph based upon triplet filters which operate on:
- an edge and
- its src and
- dst vertices.
This example could be extended to go beyond triplets by using more complex motifs.
// Select subgraph based on edges "e" of type "follow"
// pointing from a younger user "a" to an older user "b".
val paths = g.find("(a)-[e]->(b)")
.filter("e.relationship = 'follow'")
.filter("a.age < b.age")
// "paths" contains vertex info. Extract the edges.
val e2 = paths.select("e.src", "e.dst", "e.relationship")
// In Spark 1.5+, the user may simplify this call:
// val e2 = paths.select("e.*")
// Construct the subgraph
val g2 = GraphFrame(g.vertices, e2)
paths: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: struct<id: string, name: string ... 1 more field>, e: struct<src: string, dst: string ... 1 more field> ... 1 more field]
e2: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g2.edges)
| src | dst | relationship |
|---|---|---|
| c | b | follow |
| e | f | follow |
Standard graph algorithms in GraphX conveniently via GraphFrames
GraphFrames comes with a number of standard graph algorithms built in:
- Breadth-first search (BFS)
- Connected components
- Strongly connected components
- Label Propagation Algorithm (LPA)
- PageRank
- Shortest paths
- Triangle count
Read
https://graphframes.github.io/graphframes/docs/_site/user-guide.html
Search from "Esther" for users of age < 32.
// Search from "Esther" for users of age <= 32.
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32").run()
display(paths)
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther' OR name = 'Bob'").toExpr("age < 32").run()
display(paths)
The search may also be limited by edge filters and maximum path lengths.
val filteredPaths = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32")
.edgeFilter("relationship != 'friend'")
.maxPathLength(3)
.run()
display(filteredPaths)
Connected components
Compute the connected component membership of each vertex and return a graph with each vertex assigned a component ID.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components.
From https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components:-
NOTE: With GraphFrames 0.3.0 and later releases, the default Connected Components algorithm requires setting a Spark checkpoint directory. Users can revert to the old algorithm using .setAlgorithm("graphx").
Recall the following quote from Chapter 5 on Effective Transformations of the High Performance Spark Book why one needs to check-point to keep the RDD lineage DAGs from growing too large.
Types of Reuse: Cache, Persist, Checkpoint, Shuffle Files If you decide that you need to reuse your RDD, Spark provides a multitude of options for how to store the RDD. Thus it is important to understand when to use the various types of persistence.There are three primary operations that you can use to store your RDD: cache, persist, and checkpoint. In general, caching (equivalent to persisting with the in-memory storage) and persisting are most useful to avoid recomputation during one Spark job or to break RDDs with long lineages, since they keep an RDD on the executors during a Spark job. Checkpointing is most useful to prevent failures and a high cost of recomputation by saving intermediate results. Like persisting, checkpointing helps avoid computation, thus minimizing the cost of failure, and avoids recomputation by breaking the lineage graph.
sc.setCheckpointDir("/_checkpoint") // just a directory in distributed file system
val result = g.connectedComponents.run()
display(result)
| id | name | age | component |
|---|---|---|---|
| a | Alice | 34.0 | 4.12316860416e11 |
| b | Bob | 36.0 | 4.12316860416e11 |
| c | Charlie | 30.0 | 4.12316860416e11 |
| d | David | 29.0 | 4.12316860416e11 |
| e | Esther | 32.0 | 4.12316860416e11 |
| f | Fanny | 36.0 | 4.12316860416e11 |
| g | Gabby | 60.0 | 1.46028888064e11 |
Fun Exercise: Try to modify the d3.graph function to allow a visualisation of a given Sequence of component ids in the above result.
Strongly connected components
Compute the strongly connected component (SCC) of each vertex and return a graph with each vertex assigned to the SCC containing that vertex.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#strongly-connected-components.
val result = g.stronglyConnectedComponents.maxIter(10).run()
display(result.orderBy("component"))
| id | name | age | component |
|---|---|---|---|
| g | Gabby | 60.0 | 1.46028888064e11 |
| f | Fanny | 36.0 | 4.12316860416e11 |
| a | Alice | 34.0 | 6.70014898176e11 |
| e | Esther | 32.0 | 6.70014898176e11 |
| d | David | 29.0 | 6.70014898176e11 |
| b | Bob | 36.0 | 1.047972020224e12 |
| c | Charlie | 30.0 | 1.047972020224e12 |
Label propagation
Run static Label Propagation Algorithm for detecting communities in networks.
Each node in the network is initially assigned to its own community. At every superstep, nodes send their community affiliation to all neighbors and update their state to the mode community affiliation of incoming messages.
LPA is a standard community detection algorithm for graphs. It is very inexpensive computationally, although
- (1) convergence is not guaranteed and
- (2) one can end up with trivial solutions (all nodes are identified into a single community).
READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#label-propagation-algorithm-lpa.
val result = g.labelPropagation.maxIter(5).run()
display(result.orderBy("label"))
| id | name | age | label |
|---|---|---|---|
| g | Gabby | 60.0 | 1.46028888064e11 |
| b | Bob | 36.0 | 1.047972020224e12 |
| e | Esther | 32.0 | 1.382979469312e12 |
| a | Alice | 34.0 | 1.382979469312e12 |
| c | Charlie | 30.0 | 1.382979469312e12 |
| f | Fanny | 36.0 | 1.46028888064e12 |
| d | David | 29.0 | 1.46028888064e12 |
PageRank
Identify important vertices in a graph based on connections.
READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#pagerank.
// Run PageRank until convergence to tolerance "tol".
val results = g.pageRank.resetProbability(0.15).tol(0.01).run()
display(results.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 2.655507832863289 |
| e | Esther | 32.0 | 0.37085233187676075 |
| a | Alice | 34.0 | 0.44910633706538744 |
| f | Fanny | 36.0 | 0.3283606792049851 |
| g | Gabby | 60.0 | 0.1799821386239711 |
| d | David | 29.0 | 0.3283606792049851 |
| c | Charlie | 30.0 | 2.6878300011606218 |
display(results.edges)
| src | dst | relationship | weight |
|---|---|---|---|
| f | c | follow | 1.0 |
| e | f | follow | 0.5 |
| e | d | friend | 0.5 |
| d | a | friend | 1.0 |
| c | b | follow | 1.0 |
| b | c | follow | 1.0 |
| a | e | friend | 0.5 |
| a | b | friend | 0.5 |
// Run PageRank for a fixed number of iterations.
val results2 = g.pageRank.resetProbability(0.15).maxIter(10).run()
display(results2.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 2.7025217677349773 |
| e | Esther | 32.0 | 0.3613490987992571 |
| a | Alice | 34.0 | 0.4485115093698443 |
| f | Fanny | 36.0 | 0.32504910549694244 |
| g | Gabby | 60.0 | 0.17073170731707318 |
| d | David | 29.0 | 0.32504910549694244 |
| c | Charlie | 30.0 | 2.6667877057849627 |
// Run PageRank personalized for vertex "a"
val results3 = g.pageRank.resetProbability(0.15).maxIter(10).sourceId("a").run()
display(results3.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 0.3366143039702568 |
| e | Esther | 32.0 | 7.657840357273027e-2 |
| a | Alice | 34.0 | 0.17710831642683564 |
| f | Fanny | 36.0 | 3.189213697274781e-2 |
| g | Gabby | 60.0 | 0.0 |
| d | David | 29.0 | 3.189213697274781e-2 |
| c | Charlie | 30.0 | 0.3459147020846817 |
Shortest paths
Computes shortest paths to the given set of landmark vertices, where landmarks are specified by vertex ID.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#shortest-paths.
val paths = g.shortestPaths.landmarks(Seq("a", "d")).run()
display(paths)
g.edges.show()
+---+---+------------+
|src|dst|relationship|
+---+---+------------+
| a| b| friend|
| b| c| follow|
| c| b| follow|
| f| c| follow|
| e| f| follow|
| e| d| friend|
| d| a| friend|
| a| e| friend|
+---+---+------------+
Triangle count
Computes the number of triangles passing through each vertex.
val results = g.triangleCount.run()
display(results)
| count | id | name | age |
|---|---|---|---|
| 1.0 | a | Alice | 34.0 |
| 0.0 | b | Bob | 36.0 |
| 0.0 | c | Charlie | 30.0 |
| 1.0 | d | David | 29.0 |
| 1.0 | e | Esther | 32.0 |
| 0.0 | f | Fanny | 36.0 |
| 0.0 | g | Gabby | 60.0 |
YouTry
and undestand how the below code snippet shows how to use aggregateMessages to compute the sum of the ages of adjacent users.
import org.graphframes.{examples,GraphFrame}
import org.graphframes.lib.AggregateMessages
val g: GraphFrame = examples.Graphs.friends // get example graph
// We will use AggregateMessages utilities later, so name it "AM" for short.
val AM = AggregateMessages
// For each user, sum the ages of the adjacent users.
val msgToSrc = AM.dst("age")
val msgToDst = AM.src("age")
val agg = { g.aggregateMessages
.sendToSrc(msgToSrc) // send destination user's age to source
.sendToDst(msgToDst) // send source user's age to destination
.agg(sum(AM.msg).as("summedAges")) } // sum up ages, stored in AM.msg column
agg.show()
+---+----------+
| id|summedAges|
+---+----------+
| a| 97|
| c| 108|
| e| 99|
| d| 66|
| b| 94|
| f| 62|
+---+----------+
import org.graphframes.{examples, GraphFrame}
import org.graphframes.lib.AggregateMessages
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
AM: org.graphframes.lib.AggregateMessages.type = org.graphframes.lib.AggregateMessages$@706833c8
msgToSrc: org.apache.spark.sql.Column = dst[age]
msgToDst: org.apache.spark.sql.Column = src[age]
agg: org.apache.spark.sql.DataFrame = [id: string, summedAges: bigint]
There is a lot more that can be done with aggregate messaging - let's get into belief propogation algorithm for a more complex example!
Belief propogation is a powerful computational framework for Graphical Models.
- let's dive here:
as
This provides a template for building customized BP algorithms for different types of graphical models.
Project Idea
Understand parallel belief propagation using colored fields in the Scala code linked above and also pasted below in one cell (for you to modify if you want to do it in a databricks or jupyter or zeppelin notebook) unless you want to fork and extend the github repo directly with your own example.
Then use it with necessary adaptations to be able to model your favorite interacting particle system. Don't just redo the Ising model done there!
This can be used to gain intuition for various real-world scenarios, including the mathematics in your head:
- Make a graph for contact network of a set of hosts
- A simple model of COVID spreading in an SI or SIS or SIR or other epidemic models
- this can be abstract and simply show your skills in programming, say create a random network
- or be more explicit with some assumptions about the contact process (population sampled, in one or two cities, with some assumptions on contacts during transportation, school, work, etc)
- show that you have a fully scalable simulation model that can theoretically scale to billions of hosts
The project does not have to be a recommendation to Swedish authorities! Just a start in the right direction, for instance.
Some readings that can help here include the following and references therein:
- The Transmission Process: A Combinatorial Stochastic Process for the Evolution of Transmission Trees over Networks, Raazesh Sainudiin and David Welch, Journal of Theoretical Biology, Volume 410, Pages 137–170, 10.1016/j.jtbi.2016.07.038, 2016.
Other Project Ideas
- try to do a scalable inference algorithm for one of the graphical models that you already know...
- make a large simulaiton of your favourite Finite Markov Information Exchange (FMIE) process defined by Aldous (see reference in the above linked paper)
- anything else that fancies you or your research orientation/interests and can benefit from adapting the template for the parallel belief propagation algorithm here.
If you want to do this project in databricks (or other) notebook then start by modifying the following code from the example and making it run... Then adapt... start in small steps... make a team with fellow students with complementary skills...
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.graphframes.examples
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.{Graph, VertexRDD, Edge => GXEdge}
import org.apache.spark.sql.{Column, Row, SparkSession, SQLContext}
import org.apache.spark.sql.functions.{col, lit, sum, udf, when}
import org.graphframes.GraphFrame
import org.graphframes.examples.Graphs.gridIsingModel
import org.graphframes.lib.AggregateMessages
/**
* Example code for Belief Propagation (BP)
*
* This provides a template for building customized BP algorithms for different types of
* graphical models.
*
* This example:
* - Ising model on a grid
* - Parallel Belief Propagation using colored fields
*
* Ising models are probabilistic graphical models over binary variables x,,i,,.
* Each binary variable x,,i,, corresponds to one vertex, and it may take values -1 or +1.
* The probability distribution P(X) (over all x,,i,,) is parameterized by vertex factors a,,i,,
* and edge factors b,,ij,,:
* {{{
* P(X) = (1/Z) * exp[ \sum_i a_i x_i + \sum_{ij} b_{ij} x_i x_j ]
* }}}
* where Z is the normalization constant (partition function).
* See [[https://en.wikipedia.org/wiki/Ising_model Wikipedia]] for more information on Ising models.
*
* Belief Propagation (BP) provides marginal probabilities of the values of the variables x,,i,,,
* i.e., P(x,,i,,) for each i. This allows a user to understand likely values of variables.
* See [[https://en.wikipedia.org/wiki/Belief_propagation Wikipedia]] for more information on BP.
*
* We use a batch synchronous BP algorithm, where batches of vertices are updated synchronously.
* We follow the mean field update algorithm in Slide 13 of the
* [[http://www.eecs.berkeley.edu/~wainwrig/Talks/A_GraphModel_Tutorial talk slides]] from:
* Wainwright. "Graphical models, message-passing algorithms, and convex optimization."
*
* The batches are chosen according to a coloring. For background on graph colorings for inference,
* see for example:
* Gonzalez et al. "Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees."
* AISTATS, 2011.
*
* The BP algorithm works by:
* - Coloring the graph by assigning a color to each vertex such that no neighboring vertices
* share the same color.
* - In each step of BP, update all vertices of a single color. Alternate colors.
*/
object BeliefPropagation {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("BeliefPropagation example")
.getOrCreate()
val sql = spark.sqlContext
// Create graphical model g of size 3 x 3.
val g = gridIsingModel(sql, 3)
println("Original Ising model:")
g.vertices.show()
g.edges.show()
// Run BP for 5 iterations.
val numIter = 5
val results = runBPwithGraphX(g, numIter)
// Display beliefs.
val beliefs = results.vertices.select("id", "belief")
println(s"Done with BP. Final beliefs after $numIter iterations:")
beliefs.show()
spark.stop()
}
/**
* Given a GraphFrame, choose colors for each vertex. No neighboring vertices will share the
* same color. The number of colors is minimized.
*
* This is written specifically for grid graphs. For non-grid graphs, it should be generalized,
* such as by using a greedy coloring scheme.
*
* @param g Grid graph generated by [[org.graphframes.examples.Graphs.gridIsingModel()]]
* @return Same graph, but with a new vertex column "color" of type Int (0 or 1)
*/
private def colorGraph(g: GraphFrame): GraphFrame = {
val colorUDF = udf { (i: Int, j: Int) => (i + j) % 2 }
val v = g.vertices.withColumn("color", colorUDF(col("i"), col("j")))
GraphFrame(v, g.edges)
}
/**
* Run Belief Propagation.
*
* This implementation of BP shows how to use GraphX's aggregateMessages method.
* It is simple to convert to and from GraphX format. This method does the following:
* - Color GraphFrame vertices for BP scheduling.
* - Convert GraphFrame to GraphX format.
* - Run BP using GraphX's aggregateMessages API.
* - Augment the original GraphFrame with the BP results (vertex beliefs).
*
* @param g Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
* @param numIter Number of iterations of BP to run. One iteration includes updating each
* vertex's belief once.
* @return Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
* "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
* value +1 instead of -1.
*/
def runBPwithGraphX(g: GraphFrame, numIter: Int): GraphFrame = {
// Choose colors for vertices for BP scheduling.
val colorG = colorGraph(g)
val numColors: Int = colorG.vertices.select("color").distinct.count().toInt
// Convert GraphFrame to GraphX, and initialize beliefs.
val gx0 = colorG.toGraphX
// Schema maps for extracting attributes
val vColsMap = colorG.vertexColumnMap
val eColsMap = colorG.edgeColumnMap
// Convert vertex attributes to nice case classes.
val gx1: Graph[VertexAttr, Row] = gx0.mapVertices { case (_, attr) =>
// Initialize belief at 0.0
VertexAttr(attr.getDouble(vColsMap("a")), 0.0, attr.getInt(vColsMap("color")))
}
// Convert edge attributes to nice case classes.
val extractEdgeAttr: (GXEdge[Row] => EdgeAttr) = { e =>
EdgeAttr(e.attr.getDouble(eColsMap("b")))
}
var gx: Graph[VertexAttr, EdgeAttr] = gx1.mapEdges(extractEdgeAttr)
// Run BP for numIter iterations.
for (iter <- Range(0, numIter)) {
// For each color, have that color receive messages from neighbors.
for (color <- Range(0, numColors)) {
// Send messages to vertices of the current color.
val msgs: VertexRDD[Double] = gx.aggregateMessages(
ctx =>
// Can send to source or destination since edges are treated as undirected.
if (ctx.dstAttr.color == color) {
val msg = ctx.attr.b * ctx.srcAttr.belief
// Only send message if non-zero.
if (msg != 0) ctx.sendToDst(msg)
} else if (ctx.srcAttr.color == color) {
val msg = ctx.attr.b * ctx.dstAttr.belief
// Only send message if non-zero.
if (msg != 0) ctx.sendToSrc(msg)
},
_ + _)
// Receive messages, and update beliefs for vertices of the current color.
gx = gx.outerJoinVertices(msgs) {
case (vID, vAttr, optMsg) =>
if (vAttr.color == color) {
val x = vAttr.a + optMsg.getOrElse(0.0)
val newBelief = math.exp(-log1pExp(-x))
VertexAttr(vAttr.a, newBelief, color)
} else {
vAttr
}
}
}
}
// Convert back to GraphFrame with a new column "belief" for vertices DataFrame.
val gxFinal: Graph[Double, Unit] = gx.mapVertices((_, attr) => attr.belief).mapEdges(_ => ())
GraphFrame.fromGraphX(colorG, gxFinal, vertexNames = Seq("belief"))
}
case class VertexAttr(a: Double, belief: Double, color: Int)
case class EdgeAttr(b: Double)
/**
* Run Belief Propagation.
*
* This implementation of BP shows how to use GraphFrame's aggregateMessages method.
* - Color GraphFrame vertices for BP scheduling.
* - Run BP using GraphFrame's aggregateMessages API.
* - Augment the original GraphFrame with the BP results (vertex beliefs).
*
* @param g Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
* @param numIter Number of iterations of BP to run. One iteration includes updating each
* vertex's belief once.
* @return Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
* "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
* value +1 instead of -1.
*/
def runBPwithGraphFrames(g: GraphFrame, numIter: Int): GraphFrame = {
// Choose colors for vertices for BP scheduling.
val colorG = colorGraph(g)
val numColors: Int = colorG.vertices.select("color").distinct.count().toInt
// TODO: Handle vertices without any edges.
// Initialize vertex beliefs at 0.0.
var gx = GraphFrame(colorG.vertices.withColumn("belief", lit(0.0)), colorG.edges)
// Run BP for numIter iterations.
for (iter <- Range(0, numIter)) {
// For each color, have that color receive messages from neighbors.
for (color <- Range(0, numColors)) {
// Define "AM" for shorthand for referring to the src, dst, edge, and msg fields.
// (See usage below.)
val AM = AggregateMessages
// Send messages to vertices of the current color.
// We may send to source or destination since edges are treated as undirected.
val msgForSrc: Column = when(AM.src("color") === color, AM.edge("b") * AM.dst("belief"))
val msgForDst: Column = when(AM.dst("color") === color, AM.edge("b") * AM.src("belief"))
val logistic = udf { (x: Double) => math.exp(-log1pExp(-x)) }
val aggregates = gx.aggregateMessages
.sendToSrc(msgForSrc)
.sendToDst(msgForDst)
.agg(sum(AM.msg).as("aggMess"))
val v = gx.vertices
// Receive messages, and update beliefs for vertices of the current color.
val newBeliefCol = when(v("color") === color && aggregates("aggMess").isNotNull,
logistic(aggregates("aggMess") + v("a")))
.otherwise(v("belief")) // keep old beliefs for other colors
val newVertices = v
.join(aggregates, v("id") === aggregates("id"), "left_outer") // join messages, vertices
.drop(aggregates("id")) // drop duplicate ID column (from outer join)
.withColumn("newBelief", newBeliefCol) // compute new beliefs
.drop("aggMess") // drop messages
.drop("belief") // drop old beliefs
.withColumnRenamed("newBelief", "belief")
// Cache new vertices using workaround for SPARK-13346
val cachedNewVertices = AM.getCachedDataFrame(newVertices)
gx = GraphFrame(cachedNewVertices, gx.edges)
}
}
// Drop the "color" column from vertices
GraphFrame(gx.vertices.drop("color"), gx.edges)
}
/** More numerically stable `log(1 + exp(x))` */
private def log1pExp(x: Double): Double = {
if (x > 0) {
x + math.log1p(math.exp(-x))
} else {
math.log1p(math.exp(x))
}
}
}
This is a scala version of the python notebook in the following talk:
Homework:
See https://www.brighttalk.com/webcast/12891/199003 (you need to subscribe freely to Bright Talk first). Then go through this scala version of the notebook from the talk.
On-Time Flight Performance with GraphFrames for Apache Spark
This notebook provides an analysis of On-Time Flight Performance and Departure Delays data using GraphFrames for Apache Spark.
Source Data:
- OpenFlights: Airport, airline and route data
- United States Department of Transportation: Bureau of Transportation Statistics (TranStats)
- Note, the data used here was extracted from the US DOT:BTS between 1/1/2014 and 3/31/2014*
References:
Preparation
Extract the Airports and Departure Delays information from S3 / DBFS
// Set File Paths
val tripdelaysFilePath = "/datasets/sds/flights/departuredelays.csv"
val airportsnaFilePath = "/datasets/sds/flights/airport-codes-na.txt"
tripdelaysFilePath: String = /datasets/sds/flights/departuredelays.csv
airportsnaFilePath: String = /datasets/sds/flights/airport-codes-na.txt
// Obtain airports dataset
// Note that "spark-csv" package is built-in datasource in Spark 2.0
val airportsna = sqlContext.read.format("com.databricks.spark.csv").
option("header", "true").
option("inferschema", "true").
option("delimiter", "\t").
load(airportsnaFilePath)
airportsna.createOrReplaceTempView("airports_na")
// Obtain departure Delays data
val departureDelays = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load(tripdelaysFilePath)
departureDelays.createOrReplaceTempView("departureDelays")
departureDelays.cache()
// Available IATA (International Air Transport Association) codes from the departuredelays sample dataset
val tripIATA = sqlContext.sql("select distinct iata from (select distinct origin as iata from departureDelays union all select distinct destination as iata from departureDelays) a")
tripIATA.createOrReplaceTempView("tripIATA")
// Only include airports with atleast one trip from the departureDelays dataset
val airports = sqlContext.sql("select f.IATA, f.City, f.State, f.Country from airports_na f join tripIATA t on t.IATA = f.IATA")
airports.createOrReplaceTempView("airports")
airports.cache()
airportsna: org.apache.spark.sql.DataFrame = [City: string, State: string ... 2 more fields]
departureDelays: org.apache.spark.sql.DataFrame = [date: string, delay: string ... 3 more fields]
tripIATA: org.apache.spark.sql.DataFrame = [iata: string]
airports: org.apache.spark.sql.DataFrame = [IATA: string, City: string ... 2 more fields]
res0: airports.type = [IATA: string, City: string ... 2 more fields]
// Build `departureDelays_geo` DataFrame
// Obtain key attributes such as Date of flight, delays, distance, and airport information (Origin, Destination)
val departureDelays_geo = sqlContext.sql("select cast(f.date as int) as tripid, cast(concat(concat(concat(concat(concat(concat('2014-', concat(concat(substr(cast(f.date as string), 1, 2), '-')), substr(cast(f.date as string), 3, 2)), ' '), substr(cast(f.date as string), 5, 2)), ':'), substr(cast(f.date as string), 7, 2)), ':00') as timestamp) as `localdate`, cast(f.delay as int), cast(f.distance as int), f.origin as src, f.destination as dst, o.city as city_src, d.city as city_dst, o.state as state_src, d.state as state_dst from departuredelays f join airports o on o.iata = f.origin join airports d on d.iata = f.destination")
// RegisterTempTable
departureDelays_geo.createOrReplaceTempView("departureDelays_geo")
// Cache and Count
departureDelays_geo.cache()
departureDelays_geo.count()
departureDelays_geo: org.apache.spark.sql.DataFrame = [tripid: int, localdate: timestamp ... 8 more fields]
res2: Long = 1361141
display(departureDelays_geo)
| tripid | localdate | delay | distance | src | dst | city_src | city_dst | state_src | state_dst |
|---|---|---|---|---|---|---|---|---|---|
| 1011111.0 | 2014-01-01T11:11:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1021111.0 | 2014-01-02T11:11:00.000+0000 | 7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1031111.0 | 2014-01-03T11:11:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1041925.0 | 2014-01-04T19:25:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1061115.0 | 2014-01-06T11:15:00.000+0000 | 33.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1071115.0 | 2014-01-07T11:15:00.000+0000 | 23.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1081115.0 | 2014-01-08T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1091115.0 | 2014-01-09T11:15:00.000+0000 | 11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1101115.0 | 2014-01-10T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1112015.0 | 2014-01-11T20:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1121925.0 | 2014-01-12T19:25:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1131115.0 | 2014-01-13T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1141115.0 | 2014-01-14T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1151115.0 | 2014-01-15T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1161115.0 | 2014-01-16T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1171115.0 | 2014-01-17T11:15:00.000+0000 | 4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1182015.0 | 2014-01-18T20:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1191925.0 | 2014-01-19T19:25:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1201115.0 | 2014-01-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1211115.0 | 2014-01-21T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1221115.0 | 2014-01-22T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1231115.0 | 2014-01-23T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1241115.0 | 2014-01-24T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1252015.0 | 2014-01-25T20:15:00.000+0000 | -12.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1261925.0 | 2014-01-26T19:25:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1271115.0 | 2014-01-27T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1281115.0 | 2014-01-28T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1291115.0 | 2014-01-29T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1301115.0 | 2014-01-30T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1311115.0 | 2014-01-31T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2012015.0 | 2014-02-01T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2022015.0 | 2014-02-02T20:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2031115.0 | 2014-02-03T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2041115.0 | 2014-02-04T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2051115.0 | 2014-02-05T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2061115.0 | 2014-02-06T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2071115.0 | 2014-02-07T11:15:00.000+0000 | -15.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2082015.0 | 2014-02-08T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2091925.0 | 2014-02-09T19:25:00.000+0000 | 1.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2101115.0 | 2014-02-10T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2111115.0 | 2014-02-11T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2121115.0 | 2014-02-12T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2131115.0 | 2014-02-13T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2141115.0 | 2014-02-14T11:15:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2152015.0 | 2014-02-15T20:15:00.000+0000 | 16.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2161925.0 | 2014-02-16T19:25:00.000+0000 | 169.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2171115.0 | 2014-02-17T11:15:00.000+0000 | 27.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2181115.0 | 2014-02-18T11:15:00.000+0000 | 96.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2191115.0 | 2014-02-19T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2201115.0 | 2014-02-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2211115.0 | 2014-02-21T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2222015.0 | 2014-02-22T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2231925.0 | 2014-02-23T19:25:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2241115.0 | 2014-02-24T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2251115.0 | 2014-02-25T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2261115.0 | 2014-02-26T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2271115.0 | 2014-02-27T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2281115.0 | 2014-02-28T11:15:00.000+0000 | 5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3012015.0 | 2014-03-01T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3022000.0 | 2014-03-02T20:00:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3031115.0 | 2014-03-03T11:15:00.000+0000 | 17.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3041115.0 | 2014-03-04T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3051115.0 | 2014-03-05T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3061115.0 | 2014-03-06T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3071115.0 | 2014-03-07T11:15:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3082000.0 | 2014-03-08T20:00:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3092000.0 | 2014-03-09T20:00:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3101115.0 | 2014-03-10T11:15:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3111115.0 | 2014-03-11T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3121115.0 | 2014-03-12T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3131115.0 | 2014-03-13T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3141115.0 | 2014-03-14T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3152000.0 | 2014-03-15T20:00:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3162000.0 | 2014-03-16T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3171115.0 | 2014-03-17T11:15:00.000+0000 | 25.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3181115.0 | 2014-03-18T11:15:00.000+0000 | 2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3191115.0 | 2014-03-19T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3201115.0 | 2014-03-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3211115.0 | 2014-03-21T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3222000.0 | 2014-03-22T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3232000.0 | 2014-03-23T20:00:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3241115.0 | 2014-03-24T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3251115.0 | 2014-03-25T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3261115.0 | 2014-03-26T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3271115.0 | 2014-03-27T11:15:00.000+0000 | 9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3281115.0 | 2014-03-28T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3292000.0 | 2014-03-29T20:00:00.000+0000 | -19.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3302000.0 | 2014-03-30T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3311115.0 | 2014-03-31T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2011230.0 | 2014-02-01T12:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2010719.0 | 2014-02-01T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021230.0 | 2014-02-02T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2020709.0 | 2014-02-02T07:09:00.000+0000 | 59.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021654.0 | 2014-02-02T16:54:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2030719.0 | 2014-02-03T07:19:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031659.0 | 2014-02-03T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031230.0 | 2014-02-03T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2032043.0 | 2014-02-03T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041230.0 | 2014-02-04T12:30:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2040719.0 | 2014-02-04T07:19:00.000+0000 | 168.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041730.0 | 2014-02-04T17:30:00.000+0000 | 88.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2042043.0 | 2014-02-04T20:43:00.000+0000 | 106.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051659.0 | 2014-02-05T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2050719.0 | 2014-02-05T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2050929.0 | 2014-02-05T09:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2052043.0 | 2014-02-05T20:43:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2060719.0 | 2014-02-06T07:19:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061659.0 | 2014-02-06T16:59:00.000+0000 | 82.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061230.0 | 2014-02-06T12:30:00.000+0000 | 61.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2062043.0 | 2014-02-06T20:43:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2070719.0 | 2014-02-07T07:19:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071659.0 | 2014-02-07T16:59:00.000+0000 | 19.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071230.0 | 2014-02-07T12:30:00.000+0000 | 27.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2072048.0 | 2014-02-07T20:48:00.000+0000 | 47.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2081230.0 | 2014-02-08T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2080719.0 | 2014-02-08T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091229.0 | 2014-02-09T12:29:00.000+0000 | 95.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091654.0 | 2014-02-09T16:54:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2090709.0 | 2014-02-09T07:09:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2092043.0 | 2014-02-09T20:43:00.000+0000 | 32.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2100719.0 | 2014-02-10T07:19:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101659.0 | 2014-02-10T16:59:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101230.0 | 2014-02-10T12:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2102043.0 | 2014-02-10T20:43:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111230.0 | 2014-02-11T12:30:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2110719.0 | 2014-02-11T07:19:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111730.0 | 2014-02-11T17:30:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2112043.0 | 2014-02-11T20:43:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2120719.0 | 2014-02-12T07:19:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2121230.0 | 2014-02-12T12:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2120929.0 | 2014-02-12T09:29:00.000+0000 | 89.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2122043.0 | 2014-02-12T20:43:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2130738.0 | 2014-02-13T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2132041.0 | 2014-02-13T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2140729.0 | 2014-02-14T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2142041.0 | 2014-02-14T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151206.0 | 2014-02-15T12:06:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2150659.0 | 2014-02-15T06:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2160705.0 | 2014-02-16T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2170729.0 | 2014-02-17T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2172041.0 | 2014-02-17T20:41:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2180738.0 | 2014-02-18T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2182041.0 | 2014-02-18T20:41:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2190727.0 | 2014-02-19T07:27:00.000+0000 | 15.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2200738.0 | 2014-02-20T07:38:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2202041.0 | 2014-02-20T20:41:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2210729.0 | 2014-02-21T07:29:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2212041.0 | 2014-02-21T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221206.0 | 2014-02-22T12:06:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2220659.0 | 2014-02-22T06:59:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2232041.0 | 2014-02-23T20:41:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2230705.0 | 2014-02-23T07:05:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2240729.0 | 2014-02-24T07:29:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2242041.0 | 2014-02-24T20:41:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2250738.0 | 2014-02-25T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2260727.0 | 2014-02-26T07:27:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2262041.0 | 2014-02-26T20:41:00.000+0000 | 174.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2270738.0 | 2014-02-27T07:38:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2272041.0 | 2014-02-27T20:41:00.000+0000 | 32.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2280729.0 | 2014-02-28T07:29:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2282041.0 | 2014-02-28T20:41:00.000+0000 | 49.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281000.0 | 2014-02-28T10:00:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051230.0 | 2014-02-05T12:30:00.000+0000 | 216.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2131536.0 | 2014-02-13T15:36:00.000+0000 | 273.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2141536.0 | 2014-02-14T15:36:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151902.0 | 2014-02-15T19:02:00.000+0000 | 31.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151536.0 | 2014-02-15T15:36:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2162041.0 | 2014-02-16T20:41:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2161536.0 | 2014-02-16T15:36:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2171536.0 | 2014-02-17T15:36:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2181536.0 | 2014-02-18T15:36:00.000+0000 | 26.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2191536.0 | 2014-02-19T15:36:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2201536.0 | 2014-02-20T15:36:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2211536.0 | 2014-02-21T15:36:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221900.0 | 2014-02-22T19:00:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221536.0 | 2014-02-22T15:36:00.000+0000 | 65.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2231536.0 | 2014-02-23T15:36:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2241536.0 | 2014-02-24T15:36:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2251536.0 | 2014-02-25T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2261536.0 | 2014-02-26T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2271536.0 | 2014-02-27T15:36:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281536.0 | 2014-02-28T15:36:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2010730.0 | 2014-02-01T07:30:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021815.0 | 2014-02-02T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031815.0 | 2014-02-03T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041815.0 | 2014-02-04T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051815.0 | 2014-02-05T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061815.0 | 2014-02-06T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071815.0 | 2014-02-07T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2080730.0 | 2014-02-08T07:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091815.0 | 2014-02-09T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101815.0 | 2014-02-10T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111815.0 | 2014-02-11T18:15:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2121815.0 | 2014-02-12T18:15:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2131805.0 | 2014-02-13T18:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2141635.0 | 2014-02-14T16:35:00.000+0000 | 52.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2150730.0 | 2014-02-15T07:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2161635.0 | 2014-02-16T16:35:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2171635.0 | 2014-02-17T16:35:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2181635.0 | 2014-02-18T16:35:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2191635.0 | 2014-02-19T16:35:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2201635.0 | 2014-02-20T16:35:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2211635.0 | 2014-02-21T16:35:00.000+0000 | 292.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2220730.0 | 2014-02-22T07:30:00.000+0000 | 28.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2231635.0 | 2014-02-23T16:35:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2241635.0 | 2014-02-24T16:35:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2251635.0 | 2014-02-25T16:35:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2261635.0 | 2014-02-26T16:35:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2271635.0 | 2014-02-27T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281635.0 | 2014-02-28T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011206.0 | 2014-03-01T12:06:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3010659.0 | 2014-03-01T06:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3022041.0 | 2014-03-02T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3020705.0 | 2014-03-02T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3030729.0 | 2014-03-03T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3032041.0 | 2014-03-03T20:41:00.000+0000 | 113.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3040738.0 | 2014-03-04T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3050727.0 | 2014-03-05T07:27:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3052041.0 | 2014-03-05T20:41:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3060705.0 | 2014-03-06T07:05:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3061252.0 | 2014-03-06T12:52:00.000+0000 | 67.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3062100.0 | 2014-03-06T21:00:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3070705.0 | 2014-03-07T07:05:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3071252.0 | 2014-03-07T12:52:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3072100.0 | 2014-03-07T21:00:00.000+0000 | 13.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3080705.0 | 2014-03-08T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3081250.0 | 2014-03-08T12:50:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3091255.0 | 2014-03-09T12:55:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3092059.0 | 2014-03-09T20:59:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3100705.0 | 2014-03-10T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3101252.0 | 2014-03-10T12:52:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3102059.0 | 2014-03-10T20:59:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3112059.0 | 2014-03-11T20:59:00.000+0000 | 181.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3111252.0 | 2014-03-11T12:52:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3122059.0 | 2014-03-12T20:59:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3121252.0 | 2014-03-12T12:52:00.000+0000 | 161.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3130705.0 | 2014-03-13T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3131252.0 | 2014-03-13T12:52:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3132059.0 | 2014-03-13T20:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3140705.0 | 2014-03-14T07:05:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3141252.0 | 2014-03-14T12:52:00.000+0000 | 39.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3142059.0 | 2014-03-14T20:59:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3150700.0 | 2014-03-15T07:00:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3151250.0 | 2014-03-15T12:50:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3161255.0 | 2014-03-16T12:55:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3162059.0 | 2014-03-16T20:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3170705.0 | 2014-03-17T07:05:00.000+0000 | -11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3171252.0 | 2014-03-17T12:52:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3172059.0 | 2014-03-17T20:59:00.000+0000 | 132.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3182059.0 | 2014-03-18T20:59:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3181252.0 | 2014-03-18T12:52:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3192059.0 | 2014-03-19T20:59:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3191252.0 | 2014-03-19T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3200705.0 | 2014-03-20T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3201252.0 | 2014-03-20T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3202059.0 | 2014-03-20T20:59:00.000+0000 | 77.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3210705.0 | 2014-03-21T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3211252.0 | 2014-03-21T12:52:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3212059.0 | 2014-03-21T20:59:00.000+0000 | 11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3220705.0 | 2014-03-22T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3221250.0 | 2014-03-22T12:50:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3221600.0 | 2014-03-22T16:00:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3231255.0 | 2014-03-23T12:55:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3240705.0 | 2014-03-24T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3241252.0 | 2014-03-24T12:52:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3242059.0 | 2014-03-24T20:59:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3252059.0 | 2014-03-25T20:59:00.000+0000 | 121.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3251252.0 | 2014-03-25T12:52:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3262059.0 | 2014-03-26T20:59:00.000+0000 | 49.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3261252.0 | 2014-03-26T12:52:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3270705.0 | 2014-03-27T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3271252.0 | 2014-03-27T12:52:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3272059.0 | 2014-03-27T20:59:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3280705.0 | 2014-03-28T07:05:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3281252.0 | 2014-03-28T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3282059.0 | 2014-03-28T20:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3291250.0 | 2014-03-29T12:50:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3290705.0 | 2014-03-29T07:05:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3291600.0 | 2014-03-29T16:00:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3301255.0 | 2014-03-30T12:55:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3302059.0 | 2014-03-30T20:59:00.000+0000 | 166.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3310705.0 | 2014-03-31T07:05:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3311252.0 | 2014-03-31T12:52:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3312059.0 | 2014-03-31T20:59:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011902.0 | 2014-03-01T19:02:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011536.0 | 2014-03-01T15:36:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3021536.0 | 2014-03-02T15:36:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3031536.0 | 2014-03-03T15:36:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3041536.0 | 2014-03-04T15:36:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3051536.0 | 2014-03-05T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3090715.0 | 2014-03-09T07:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3110705.0 | 2014-03-11T07:05:00.000+0000 | -11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3120659.0 | 2014-03-12T06:59:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3160715.0 | 2014-03-16T07:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3180705.0 | 2014-03-18T07:05:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3190659.0 | 2014-03-19T06:59:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3230715.0 | 2014-03-23T07:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3250705.0 | 2014-03-25T07:05:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3260659.0 | 2014-03-26T06:59:00.000+0000 | 63.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3300715.0 | 2014-03-30T07:15:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3010730.0 | 2014-03-01T07:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3021635.0 | 2014-03-02T16:35:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3031635.0 | 2014-03-03T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3041635.0 | 2014-03-04T16:35:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3051635.0 | 2014-03-05T16:35:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3061635.0 | 2014-03-06T16:35:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3071635.0 | 2014-03-07T16:35:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3080730.0 | 2014-03-08T07:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3091825.0 | 2014-03-09T18:25:00.000+0000 | 45.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3101825.0 | 2014-03-10T18:25:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3111825.0 | 2014-03-11T18:25:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3121825.0 | 2014-03-12T18:25:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3131825.0 | 2014-03-13T18:25:00.000+0000 | 123.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3141825.0 | 2014-03-14T18:25:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3150730.0 | 2014-03-15T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3161825.0 | 2014-03-16T18:25:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3171825.0 | 2014-03-17T18:25:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3181825.0 | 2014-03-18T18:25:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3191825.0 | 2014-03-19T18:25:00.000+0000 | 223.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3201825.0 | 2014-03-20T18:25:00.000+0000 | 178.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3211825.0 | 2014-03-21T18:25:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3220730.0 | 2014-03-22T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3231825.0 | 2014-03-23T18:25:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3241825.0 | 2014-03-24T18:25:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3251825.0 | 2014-03-25T18:25:00.000+0000 | 222.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3261825.0 | 2014-03-26T18:25:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3271825.0 | 2014-03-27T18:25:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3281825.0 | 2014-03-28T18:25:00.000+0000 | 26.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3290730.0 | 2014-03-29T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3301825.0 | 2014-03-30T18:25:00.000+0000 | 139.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3311825.0 | 2014-03-31T18:25:00.000+0000 | 25.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1020705.0 | 2014-01-02T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1030705.0 | 2014-01-03T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1040655.0 | 2014-01-04T06:55:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1050703.0 | 2014-01-05T07:03:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1060705.0 | 2014-01-06T07:05:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071230.0 | 2014-01-07T12:30:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1070719.0 | 2014-01-07T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071730.0 | 2014-01-07T17:30:00.000+0000 | 161.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1072043.0 | 2014-01-07T20:43:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1080719.0 | 2014-01-08T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081659.0 | 2014-01-08T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081230.0 | 2014-01-08T12:30:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1082043.0 | 2014-01-08T20:43:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1090719.0 | 2014-01-09T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091659.0 | 2014-01-09T16:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091230.0 | 2014-01-09T12:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1092043.0 | 2014-01-09T20:43:00.000+0000 | 63.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1100719.0 | 2014-01-10T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101659.0 | 2014-01-10T16:59:00.000+0000 | 244.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101230.0 | 2014-01-10T12:30:00.000+0000 | 110.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1102043.0 | 2014-01-10T20:43:00.000+0000 | 43.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1111230.0 | 2014-01-11T12:30:00.000+0000 | 87.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1110719.0 | 2014-01-11T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121654.0 | 2014-01-12T16:54:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121230.0 | 2014-01-12T12:30:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1120709.0 | 2014-01-12T07:09:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1122043.0 | 2014-01-12T20:43:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1130719.0 | 2014-01-13T07:19:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131659.0 | 2014-01-13T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131230.0 | 2014-01-13T12:30:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1132043.0 | 2014-01-13T20:43:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141230.0 | 2014-01-14T12:30:00.000+0000 | 30.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1140719.0 | 2014-01-14T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141730.0 | 2014-01-14T17:30:00.000+0000 | 69.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1142043.0 | 2014-01-14T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1150719.0 | 2014-01-15T07:19:00.000+0000 | 42.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151659.0 | 2014-01-15T16:59:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151230.0 | 2014-01-15T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1152043.0 | 2014-01-15T20:43:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1160719.0 | 2014-01-16T07:19:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161659.0 | 2014-01-16T16:59:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161230.0 | 2014-01-16T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1162043.0 | 2014-01-16T20:43:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171659.0 | 2014-01-17T16:59:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1170719.0 | 2014-01-17T07:19:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1172043.0 | 2014-01-17T20:43:00.000+0000 | 54.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1181230.0 | 2014-01-18T12:30:00.000+0000 | 20.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1180719.0 | 2014-01-18T07:19:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191654.0 | 2014-01-19T16:54:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191230.0 | 2014-01-19T12:30:00.000+0000 | 29.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1190709.0 | 2014-01-19T07:09:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1192043.0 | 2014-01-19T20:43:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201659.0 | 2014-01-20T16:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1200719.0 | 2014-01-20T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1202043.0 | 2014-01-20T20:43:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211230.0 | 2014-01-21T12:30:00.000+0000 | 102.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1210719.0 | 2014-01-21T07:19:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211730.0 | 2014-01-21T17:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1212043.0 | 2014-01-21T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1220719.0 | 2014-01-22T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221659.0 | 2014-01-22T16:59:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221230.0 | 2014-01-22T12:30:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1222043.0 | 2014-01-22T20:43:00.000+0000 | 70.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1230719.0 | 2014-01-23T07:19:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231659.0 | 2014-01-23T16:59:00.000+0000 | 94.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231230.0 | 2014-01-23T12:30:00.000+0000 | 111.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1232043.0 | 2014-01-23T20:43:00.000+0000 | 13.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1240719.0 | 2014-01-24T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241659.0 | 2014-01-24T16:59:00.000+0000 | 84.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241230.0 | 2014-01-24T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1242043.0 | 2014-01-24T20:43:00.000+0000 | 56.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1251230.0 | 2014-01-25T12:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1250719.0 | 2014-01-25T07:19:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261654.0 | 2014-01-26T16:54:00.000+0000 | 113.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261230.0 | 2014-01-26T12:30:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1260709.0 | 2014-01-26T07:09:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1262043.0 | 2014-01-26T20:43:00.000+0000 | 31.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1271659.0 | 2014-01-27T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1270719.0 | 2014-01-27T07:19:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281230.0 | 2014-01-28T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1280719.0 | 2014-01-28T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281730.0 | 2014-01-28T17:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1282043.0 | 2014-01-28T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291659.0 | 2014-01-29T16:59:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1290719.0 | 2014-01-29T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1292043.0 | 2014-01-29T20:43:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1300719.0 | 2014-01-30T07:19:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301659.0 | 2014-01-30T16:59:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301230.0 | 2014-01-30T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1302043.0 | 2014-01-30T20:43:00.000+0000 | 93.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1310719.0 | 2014-01-31T07:19:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311659.0 | 2014-01-31T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311230.0 | 2014-01-31T12:30:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1312043.0 | 2014-01-31T20:43:00.000+0000 | 28.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171230.0 | 2014-01-17T12:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201250.0 | 2014-01-20T12:50:00.000+0000 | -12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291230.0 | 2014-01-29T12:30:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1011815.0 | 2014-01-01T18:15:00.000+0000 | 125.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1021815.0 | 2014-01-02T18:15:00.000+0000 | 33.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1031815.0 | 2014-01-03T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1040755.0 | 2014-01-04T07:55:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1051815.0 | 2014-01-05T18:15:00.000+0000 | 172.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1061815.0 | 2014-01-06T18:15:00.000+0000 | 151.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071815.0 | 2014-01-07T18:15:00.000+0000 | 43.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081815.0 | 2014-01-08T18:15:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091815.0 | 2014-01-09T18:15:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101815.0 | 2014-01-10T18:15:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1110730.0 | 2014-01-11T07:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121815.0 | 2014-01-12T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131815.0 | 2014-01-13T18:15:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141815.0 | 2014-01-14T18:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151815.0 | 2014-01-15T18:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161815.0 | 2014-01-16T18:15:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171815.0 | 2014-01-17T18:15:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1180730.0 | 2014-01-18T07:30:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191815.0 | 2014-01-19T18:15:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201815.0 | 2014-01-20T18:15:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211815.0 | 2014-01-21T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221815.0 | 2014-01-22T18:15:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231815.0 | 2014-01-23T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241815.0 | 2014-01-24T18:15:00.000+0000 | 84.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1250730.0 | 2014-01-25T07:30:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261815.0 | 2014-01-26T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1271815.0 | 2014-01-27T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281815.0 | 2014-01-28T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291815.0 | 2014-01-29T18:15:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301815.0 | 2014-01-30T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311815.0 | 2014-01-31T18:15:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2011055.0 | 2014-02-01T10:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2021025.0 | 2014-02-02T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2031025.0 | 2014-02-03T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2031750.0 | 2014-02-03T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2041025.0 | 2014-02-04T10:25:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2041750.0 | 2014-02-04T17:50:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2051025.0 | 2014-02-05T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2051750.0 | 2014-02-05T17:50:00.000+0000 | 29.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2061025.0 | 2014-02-06T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2061750.0 | 2014-02-06T17:50:00.000+0000 | 48.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2071025.0 | 2014-02-07T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2071750.0 | 2014-02-07T17:50:00.000+0000 | 20.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2081055.0 | 2014-02-08T10:55:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2091025.0 | 2014-02-09T10:25:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2091750.0 | 2014-02-09T17:50:00.000+0000 | 33.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2101025.0 | 2014-02-10T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2101750.0 | 2014-02-10T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2111025.0 | 2014-02-11T10:25:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2111750.0 | 2014-02-11T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2121025.0 | 2014-02-12T10:25:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2121750.0 | 2014-02-12T17:50:00.000+0000 | 158.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2131855.0 | 2014-02-13T18:55:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2131215.0 | 2014-02-13T12:15:00.000+0000 | 23.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2141855.0 | 2014-02-14T18:55:00.000+0000 | 22.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2141215.0 | 2014-02-14T12:15:00.000+0000 | 18.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2150935.0 | 2014-02-15T09:35:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2151635.0 | 2014-02-15T16:35:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2161855.0 | 2014-02-16T18:55:00.000+0000 | 58.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2161215.0 | 2014-02-16T12:15:00.000+0000 | 17.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2171855.0 | 2014-02-17T18:55:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2171215.0 | 2014-02-17T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2181855.0 | 2014-02-18T18:55:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2181215.0 | 2014-02-18T12:15:00.000+0000 | 58.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2191855.0 | 2014-02-19T18:55:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2191215.0 | 2014-02-19T12:15:00.000+0000 | 32.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2201855.0 | 2014-02-20T18:55:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2201215.0 | 2014-02-20T12:15:00.000+0000 | 28.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2211855.0 | 2014-02-21T18:55:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2211215.0 | 2014-02-21T12:15:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2220935.0 | 2014-02-22T09:35:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2221635.0 | 2014-02-22T16:35:00.000+0000 | 133.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2231855.0 | 2014-02-23T18:55:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2231215.0 | 2014-02-23T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2241855.0 | 2014-02-24T18:55:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2241215.0 | 2014-02-24T12:15:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2251855.0 | 2014-02-25T18:55:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2251215.0 | 2014-02-25T12:15:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2261855.0 | 2014-02-26T18:55:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2261215.0 | 2014-02-26T12:15:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2271855.0 | 2014-02-27T18:55:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2271215.0 | 2014-02-27T12:15:00.000+0000 | 32.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2281855.0 | 2014-02-28T18:55:00.000+0000 | 61.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2281215.0 | 2014-02-28T12:15:00.000+0000 | 94.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3010935.0 | 2014-03-01T09:35:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3011635.0 | 2014-03-01T16:35:00.000+0000 | 39.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3021855.0 | 2014-03-02T18:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3021215.0 | 2014-03-02T12:15:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3031855.0 | 2014-03-03T18:55:00.000+0000 | 25.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3031215.0 | 2014-03-03T12:15:00.000+0000 | 8.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3041855.0 | 2014-03-04T18:55:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3041215.0 | 2014-03-04T12:15:00.000+0000 | 28.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3051855.0 | 2014-03-05T18:55:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3051215.0 | 2014-03-05T12:15:00.000+0000 | 27.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3061855.0 | 2014-03-06T18:55:00.000+0000 | 20.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3061215.0 | 2014-03-06T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3071855.0 | 2014-03-07T18:55:00.000+0000 | 36.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3071215.0 | 2014-03-07T12:15:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3081940.0 | 2014-03-08T19:40:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3080830.0 | 2014-03-08T08:30:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3091905.0 | 2014-03-09T19:05:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3090840.0 | 2014-03-09T08:40:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3101905.0 | 2014-03-10T19:05:00.000+0000 | 49.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3100840.0 | 2014-03-10T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3111905.0 | 2014-03-11T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3110840.0 | 2014-03-11T08:40:00.000+0000 | 84.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3121905.0 | 2014-03-12T19:05:00.000+0000 | 26.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3120840.0 | 2014-03-12T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3131905.0 | 2014-03-13T19:05:00.000+0000 | 37.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3130840.0 | 2014-03-13T08:40:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3141905.0 | 2014-03-14T19:05:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3140840.0 | 2014-03-14T08:40:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3150830.0 | 2014-03-15T08:30:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3151940.0 | 2014-03-15T19:40:00.000+0000 | 95.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3161905.0 | 2014-03-16T19:05:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3160840.0 | 2014-03-16T08:40:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3171905.0 | 2014-03-17T19:05:00.000+0000 | 13.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3170840.0 | 2014-03-17T08:40:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3181905.0 | 2014-03-18T19:05:00.000+0000 | 52.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3180840.0 | 2014-03-18T08:40:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3191905.0 | 2014-03-19T19:05:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3190840.0 | 2014-03-19T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3201905.0 | 2014-03-20T19:05:00.000+0000 | 36.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3200840.0 | 2014-03-20T08:40:00.000+0000 | 68.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3211905.0 | 2014-03-21T19:05:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3210840.0 | 2014-03-21T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3220830.0 | 2014-03-22T08:30:00.000+0000 | 12.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3221940.0 | 2014-03-22T19:40:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3231905.0 | 2014-03-23T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3230840.0 | 2014-03-23T08:40:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3241905.0 | 2014-03-24T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3240840.0 | 2014-03-24T08:40:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3251905.0 | 2014-03-25T19:05:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3250840.0 | 2014-03-25T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3261905.0 | 2014-03-26T19:05:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3260840.0 | 2014-03-26T08:40:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3271905.0 | 2014-03-27T19:05:00.000+0000 | 13.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3270840.0 | 2014-03-27T08:40:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3281905.0 | 2014-03-28T19:05:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3280840.0 | 2014-03-28T08:40:00.000+0000 | 33.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3290830.0 | 2014-03-29T08:30:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3291940.0 | 2014-03-29T19:40:00.000+0000 | 231.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3301905.0 | 2014-03-30T19:05:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3300840.0 | 2014-03-30T08:40:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3311905.0 | 2014-03-31T19:05:00.000+0000 | 19.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3310840.0 | 2014-03-31T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1010850.0 | 2014-01-01T08:50:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1011755.0 | 2014-01-01T17:55:00.000+0000 | 160.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1021805.0 | 2014-01-02T18:05:00.000+0000 | 138.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1020905.0 | 2014-01-02T09:05:00.000+0000 | 5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1031805.0 | 2014-01-03T18:05:00.000+0000 | 154.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1030905.0 | 2014-01-03T09:05:00.000+0000 | 179.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1041655.0 | 2014-01-04T16:55:00.000+0000 | 113.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1040900.0 | 2014-01-04T09:00:00.000+0000 | 56.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1050905.0 | 2014-01-05T09:05:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1051805.0 | 2014-01-05T18:05:00.000+0000 | 53.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1061755.0 | 2014-01-06T17:55:00.000+0000 | 61.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1060905.0 | 2014-01-06T09:05:00.000+0000 | 23.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1071025.0 | 2014-01-07T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1071750.0 | 2014-01-07T17:50:00.000+0000 | 302.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1081025.0 | 2014-01-08T10:25:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1081750.0 | 2014-01-08T17:50:00.000+0000 | 52.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1091025.0 | 2014-01-09T10:25:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1091750.0 | 2014-01-09T17:50:00.000+0000 | 8.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1101025.0 | 2014-01-10T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1101750.0 | 2014-01-10T17:50:00.000+0000 | 92.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1111055.0 | 2014-01-11T10:55:00.000+0000 | 31.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1121025.0 | 2014-01-12T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1121750.0 | 2014-01-12T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1131025.0 | 2014-01-13T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1131750.0 | 2014-01-13T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1141025.0 | 2014-01-14T10:25:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1141750.0 | 2014-01-14T17:50:00.000+0000 | 127.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1151025.0 | 2014-01-15T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1151750.0 | 2014-01-15T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1161025.0 | 2014-01-16T10:25:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1161750.0 | 2014-01-16T17:50:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1171025.0 | 2014-01-17T10:25:00.000+0000 | 12.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1171750.0 | 2014-01-17T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1181055.0 | 2014-01-18T10:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1191025.0 | 2014-01-19T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1191750.0 | 2014-01-19T17:50:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1201025.0 | 2014-01-20T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1201750.0 | 2014-01-20T17:50:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1211025.0 | 2014-01-21T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1211750.0 | 2014-01-21T17:50:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1221025.0 | 2014-01-22T10:25:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1221750.0 | 2014-01-22T17:50:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1231025.0 | 2014-01-23T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1231750.0 | 2014-01-23T17:50:00.000+0000 | 30.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1241025.0 | 2014-01-24T10:25:00.000+0000 | 43.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1241750.0 | 2014-01-24T17:50:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1251055.0 | 2014-01-25T10:55:00.000+0000 | 5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1261025.0 | 2014-01-26T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1261750.0 | 2014-01-26T17:50:00.000+0000 | 27.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1271025.0 | 2014-01-27T10:25:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1271750.0 | 2014-01-27T17:50:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1281025.0 | 2014-01-28T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1281750.0 | 2014-01-28T17:50:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1291025.0 | 2014-01-29T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1291750.0 | 2014-01-29T17:50:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1301025.0 | 2014-01-30T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1301750.0 | 2014-01-30T17:50:00.000+0000 | 35.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1311025.0 | 2014-01-31T10:25:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1311750.0 | 2014-01-31T17:50:00.000+0000 | 25.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2011530.0 | 2014-02-01T15:30:00.000+0000 | -3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2021105.0 | 2014-02-02T11:05:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2031105.0 | 2014-02-03T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2041105.0 | 2014-02-04T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2051105.0 | 2014-02-05T11:05:00.000+0000 | 6.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2061105.0 | 2014-02-06T11:05:00.000+0000 | 40.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2071105.0 | 2014-02-07T11:05:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2081530.0 | 2014-02-08T15:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2091105.0 | 2014-02-09T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2101105.0 | 2014-02-10T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2111105.0 | 2014-02-11T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2121105.0 | 2014-02-12T11:05:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2130800.0 | 2014-02-13T08:00:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2140830.0 | 2014-02-14T08:30:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2150750.0 | 2014-02-15T07:50:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2160930.0 | 2014-02-16T09:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2170830.0 | 2014-02-17T08:30:00.000+0000 | 10.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2180830.0 | 2014-02-18T08:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2190830.0 | 2014-02-19T08:30:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2200830.0 | 2014-02-20T08:30:00.000+0000 | 9.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2210830.0 | 2014-02-21T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2220750.0 | 2014-02-22T07:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2230930.0 | 2014-02-23T09:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2240830.0 | 2014-02-24T08:30:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2250830.0 | 2014-02-25T08:30:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2260830.0 | 2014-02-26T08:30:00.000+0000 | 251.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2270830.0 | 2014-02-27T08:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2280830.0 | 2014-02-28T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3010750.0 | 2014-03-01T07:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3020930.0 | 2014-03-02T09:30:00.000+0000 | 35.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3030830.0 | 2014-03-03T08:30:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3040830.0 | 2014-03-04T08:30:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3050830.0 | 2014-03-05T08:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3060830.0 | 2014-03-06T08:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3070830.0 | 2014-03-07T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3081610.0 | 2014-03-08T16:10:00.000+0000 | 93.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3090950.0 | 2014-03-09T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3100950.0 | 2014-03-10T09:50:00.000+0000 | 8.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3110950.0 | 2014-03-11T09:50:00.000+0000 | 36.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3120950.0 | 2014-03-12T09:50:00.000+0000 | 68.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3130950.0 | 2014-03-13T09:50:00.000+0000 | 13.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3140950.0 | 2014-03-14T09:50:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3151610.0 | 2014-03-15T16:10:00.000+0000 | 16.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3160950.0 | 2014-03-16T09:50:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3170950.0 | 2014-03-17T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3180950.0 | 2014-03-18T09:50:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3190950.0 | 2014-03-19T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3200950.0 | 2014-03-20T09:50:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3210950.0 | 2014-03-21T09:50:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3221610.0 | 2014-03-22T16:10:00.000+0000 | 38.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3230950.0 | 2014-03-23T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3240950.0 | 2014-03-24T09:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3250950.0 | 2014-03-25T09:50:00.000+0000 | 4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3260950.0 | 2014-03-26T09:50:00.000+0000 | 5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3270950.0 | 2014-03-27T09:50:00.000+0000 | 12.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3280950.0 | 2014-03-28T09:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3291610.0 | 2014-03-29T16:10:00.000+0000 | 12.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3300950.0 | 2014-03-30T09:50:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3310950.0 | 2014-03-31T09:50:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1011635.0 | 2014-01-01T16:35:00.000+0000 | -7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1021635.0 | 2014-01-02T16:35:00.000+0000 | 96.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1031635.0 | 2014-01-03T16:35:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1041205.0 | 2014-01-04T12:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1051635.0 | 2014-01-05T16:35:00.000+0000 | 60.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1061635.0 | 2014-01-06T16:35:00.000+0000 | -7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1071105.0 | 2014-01-07T11:05:00.000+0000 | 14.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1081105.0 | 2014-01-08T11:05:00.000+0000 | 4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1091105.0 | 2014-01-09T11:05:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1101105.0 | 2014-01-10T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1111530.0 | 2014-01-11T15:30:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1121105.0 | 2014-01-12T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1131105.0 | 2014-01-13T11:05:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1141105.0 | 2014-01-14T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1151105.0 | 2014-01-15T11:05:00.000+0000 | 9.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1161105.0 | 2014-01-16T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1171105.0 | 2014-01-17T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1181530.0 | 2014-01-18T15:30:00.000+0000 | 48.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1191105.0 | 2014-01-19T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1201105.0 | 2014-01-20T11:05:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1211105.0 | 2014-01-21T11:05:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1221105.0 | 2014-01-22T11:05:00.000+0000 | 19.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1231105.0 | 2014-01-23T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1241105.0 | 2014-01-24T11:05:00.000+0000 | 72.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1251530.0 | 2014-01-25T15:30:00.000+0000 | 5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1261105.0 | 2014-01-26T11:05:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1271105.0 | 2014-01-27T11:05:00.000+0000 | -6.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1281105.0 | 2014-01-28T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1291105.0 | 2014-01-29T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1301105.0 | 2014-01-30T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1311105.0 | 2014-01-31T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3011530.0 | 2014-03-01T15:30:00.000+0000 | 72.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3010850.0 | 2014-03-01T08:50:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3011245.0 | 2014-03-01T12:45:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021610.0 | 2014-03-02T16:10:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021350.0 | 2014-03-02T13:50:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021800.0 | 2014-03-02T18:00:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031610.0 | 2014-03-03T16:10:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3030810.0 | 2014-03-03T08:10:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031350.0 | 2014-03-03T13:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031800.0 | 2014-03-03T18:00:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041610.0 | 2014-03-04T16:10:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3040810.0 | 2014-03-04T08:10:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041350.0 | 2014-03-04T13:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041800.0 | 2014-03-04T18:00:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051610.0 | 2014-03-05T16:10:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3050810.0 | 2014-03-05T08:10:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051350.0 | 2014-03-05T13:50:00.000+0000 | 48.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051800.0 | 2014-03-05T18:00:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061610.0 | 2014-03-06T16:10:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3060810.0 | 2014-03-06T08:10:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061350.0 | 2014-03-06T13:50:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061800.0 | 2014-03-06T18:00:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071610.0 | 2014-03-07T16:10:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3070810.0 | 2014-03-07T08:10:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071350.0 | 2014-03-07T13:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071800.0 | 2014-03-07T18:00:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3081540.0 | 2014-03-08T15:40:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3081335.0 | 2014-03-08T13:35:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3080945.0 | 2014-03-08T09:45:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091620.0 | 2014-03-09T16:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091820.0 | 2014-03-09T18:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091420.0 | 2014-03-09T14:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3100820.0 | 2014-03-10T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101420.0 | 2014-03-10T14:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101820.0 | 2014-03-10T18:20:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101620.0 | 2014-03-10T16:20:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3110820.0 | 2014-03-11T08:20:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111420.0 | 2014-03-11T14:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111820.0 | 2014-03-11T18:20:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111620.0 | 2014-03-11T16:20:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3120820.0 | 2014-03-12T08:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121420.0 | 2014-03-12T14:20:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121820.0 | 2014-03-12T18:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121620.0 | 2014-03-12T16:20:00.000+0000 | 24.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3130820.0 | 2014-03-13T08:20:00.000+0000 | 126.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131420.0 | 2014-03-13T14:20:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131820.0 | 2014-03-13T18:20:00.000+0000 | 55.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131620.0 | 2014-03-13T16:20:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3140820.0 | 2014-03-14T08:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141420.0 | 2014-03-14T14:20:00.000+0000 | 35.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141820.0 | 2014-03-14T18:20:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141620.0 | 2014-03-14T16:20:00.000+0000 | 36.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3151335.0 | 2014-03-15T13:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3150945.0 | 2014-03-15T09:45:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3151540.0 | 2014-03-15T15:40:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161620.0 | 2014-03-16T16:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161820.0 | 2014-03-16T18:20:00.000+0000 | 39.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161420.0 | 2014-03-16T14:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3170820.0 | 2014-03-17T08:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171420.0 | 2014-03-17T14:20:00.000+0000 | 112.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171820.0 | 2014-03-17T18:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171620.0 | 2014-03-17T16:20:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3180820.0 | 2014-03-18T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181420.0 | 2014-03-18T14:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181820.0 | 2014-03-18T18:20:00.000+0000 | 36.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181620.0 | 2014-03-18T16:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3190820.0 | 2014-03-19T08:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191420.0 | 2014-03-19T14:20:00.000+0000 | 54.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191820.0 | 2014-03-19T18:20:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191620.0 | 2014-03-19T16:20:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3200820.0 | 2014-03-20T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201420.0 | 2014-03-20T14:20:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201820.0 | 2014-03-20T18:20:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201620.0 | 2014-03-20T16:20:00.000+0000 | 79.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3210820.0 | 2014-03-21T08:20:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211420.0 | 2014-03-21T14:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211820.0 | 2014-03-21T18:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211620.0 | 2014-03-21T16:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3221335.0 | 2014-03-22T13:35:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3220945.0 | 2014-03-22T09:45:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3221540.0 | 2014-03-22T15:40:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231620.0 | 2014-03-23T16:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231820.0 | 2014-03-23T18:20:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231420.0 | 2014-03-23T14:20:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3240820.0 | 2014-03-24T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241420.0 | 2014-03-24T14:20:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241820.0 | 2014-03-24T18:20:00.000+0000 | 44.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241620.0 | 2014-03-24T16:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3250820.0 | 2014-03-25T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251420.0 | 2014-03-25T14:20:00.000+0000 | 70.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251820.0 | 2014-03-25T18:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251620.0 | 2014-03-25T16:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3260820.0 | 2014-03-26T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261420.0 | 2014-03-26T14:20:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261820.0 | 2014-03-26T18:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261620.0 | 2014-03-26T16:20:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3270820.0 | 2014-03-27T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271420.0 | 2014-03-27T14:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271820.0 | 2014-03-27T18:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271620.0 | 2014-03-27T16:20:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3280820.0 | 2014-03-28T08:20:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281420.0 | 2014-03-28T14:20:00.000+0000 | 52.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281820.0 | 2014-03-28T18:20:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281620.0 | 2014-03-28T16:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3291335.0 | 2014-03-29T13:35:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3290945.0 | 2014-03-29T09:45:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3291540.0 | 2014-03-29T15:40:00.000+0000 | 35.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301620.0 | 2014-03-30T16:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301820.0 | 2014-03-30T18:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301420.0 | 2014-03-30T14:20:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3310820.0 | 2014-03-31T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311420.0 | 2014-03-31T14:20:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311820.0 | 2014-03-31T18:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311620.0 | 2014-03-31T16:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1010800.0 | 2014-01-01T08:00:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1011210.0 | 2014-01-01T12:10:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1011835.0 | 2014-01-01T18:35:00.000+0000 | 53.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1021215.0 | 2014-01-02T12:15:00.000+0000 | 43.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1021835.0 | 2014-01-02T18:35:00.000+0000 | 200.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1020800.0 | 2014-01-02T08:00:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1031215.0 | 2014-01-03T12:15:00.000+0000 | 185.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1031835.0 | 2014-01-03T18:35:00.000+0000 | 226.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1030800.0 | 2014-01-03T08:00:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1041420.0 | 2014-01-04T14:20:00.000+0000 | 174.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1040835.0 | 2014-01-04T08:35:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1051215.0 | 2014-01-05T12:15:00.000+0000 | 85.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1051835.0 | 2014-01-05T18:35:00.000+0000 | 283.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1050800.0 | 2014-01-05T08:00:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1061215.0 | 2014-01-06T12:15:00.000+0000 | 150.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1061835.0 | 2014-01-06T18:35:00.000+0000 | 133.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1060800.0 | 2014-01-06T08:00:00.000+0000 | 102.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071240.0 | 2014-01-07T12:40:00.000+0000 | 57.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071455.0 | 2014-01-07T14:55:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1070905.0 | 2014-01-07T09:05:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071735.0 | 2014-01-07T17:35:00.000+0000 | 131.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081240.0 | 2014-01-08T12:40:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081455.0 | 2014-01-08T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1080905.0 | 2014-01-08T09:05:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081735.0 | 2014-01-08T17:35:00.000+0000 | 34.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091240.0 | 2014-01-09T12:40:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091455.0 | 2014-01-09T14:55:00.000+0000 | 24.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1090905.0 | 2014-01-09T09:05:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091735.0 | 2014-01-09T17:35:00.000+0000 | 28.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101240.0 | 2014-01-10T12:40:00.000+0000 | 50.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101455.0 | 2014-01-10T14:55:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1100905.0 | 2014-01-10T09:05:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101735.0 | 2014-01-10T17:35:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1111305.0 | 2014-01-11T13:05:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1111805.0 | 2014-01-11T18:05:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1110950.0 | 2014-01-11T09:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121235.0 | 2014-01-12T12:35:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121455.0 | 2014-01-12T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121735.0 | 2014-01-12T17:35:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131240.0 | 2014-01-13T12:40:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131455.0 | 2014-01-13T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1130850.0 | 2014-01-13T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131735.0 | 2014-01-13T17:35:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141240.0 | 2014-01-14T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141455.0 | 2014-01-14T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1140850.0 | 2014-01-14T08:50:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141735.0 | 2014-01-14T17:35:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151240.0 | 2014-01-15T12:40:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151455.0 | 2014-01-15T14:55:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1150850.0 | 2014-01-15T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151735.0 | 2014-01-15T17:35:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161240.0 | 2014-01-16T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161455.0 | 2014-01-16T14:55:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1160850.0 | 2014-01-16T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161735.0 | 2014-01-16T17:35:00.000+0000 | 52.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171240.0 | 2014-01-17T12:40:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171455.0 | 2014-01-17T14:55:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1170850.0 | 2014-01-17T08:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171735.0 | 2014-01-17T17:35:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1181305.0 | 2014-01-18T13:05:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1181805.0 | 2014-01-18T18:05:00.000+0000 | 42.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1180950.0 | 2014-01-18T09:50:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191235.0 | 2014-01-19T12:35:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191455.0 | 2014-01-19T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191735.0 | 2014-01-19T17:35:00.000+0000 | 64.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201240.0 | 2014-01-20T12:40:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201455.0 | 2014-01-20T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1200850.0 | 2014-01-20T08:50:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201735.0 | 2014-01-20T17:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211240.0 | 2014-01-21T12:40:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211455.0 | 2014-01-21T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1210850.0 | 2014-01-21T08:50:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211735.0 | 2014-01-21T17:35:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221240.0 | 2014-01-22T12:40:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221455.0 | 2014-01-22T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1220850.0 | 2014-01-22T08:50:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221735.0 | 2014-01-22T17:35:00.000+0000 | 118.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231240.0 | 2014-01-23T12:40:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231455.0 | 2014-01-23T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1230850.0 | 2014-01-23T08:50:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231735.0 | 2014-01-23T17:35:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241240.0 | 2014-01-24T12:40:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241455.0 | 2014-01-24T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1240850.0 | 2014-01-24T08:50:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241735.0 | 2014-01-24T17:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1251305.0 | 2014-01-25T13:05:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1251805.0 | 2014-01-25T18:05:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1250950.0 | 2014-01-25T09:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261235.0 | 2014-01-26T12:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261455.0 | 2014-01-26T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261735.0 | 2014-01-26T17:35:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271240.0 | 2014-01-27T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271455.0 | 2014-01-27T14:55:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1270850.0 | 2014-01-27T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271735.0 | 2014-01-27T17:35:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281240.0 | 2014-01-28T12:40:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281455.0 | 2014-01-28T14:55:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1280850.0 | 2014-01-28T08:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281735.0 | 2014-01-28T17:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291240.0 | 2014-01-29T12:40:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291455.0 | 2014-01-29T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1290850.0 | 2014-01-29T08:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291735.0 | 2014-01-29T17:35:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301240.0 | 2014-01-30T12:40:00.000+0000 | 38.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301455.0 | 2014-01-30T14:55:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1300850.0 | 2014-01-30T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301735.0 | 2014-01-30T17:35:00.000+0000 | 57.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311240.0 | 2014-01-31T12:40:00.000+0000 | 31.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311455.0 | 2014-01-31T14:55:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1310850.0 | 2014-01-31T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311735.0 | 2014-01-31T17:35:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2011305.0 | 2014-02-01T13:05:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2011805.0 | 2014-02-01T18:05:00.000+0000 | -6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2010950.0 | 2014-02-01T09:50:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021455.0 | 2014-02-02T14:55:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021235.0 | 2014-02-02T12:35:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021650.0 | 2014-02-02T16:50:00.000+0000 | 77.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031240.0 | 2014-02-03T12:40:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031455.0 | 2014-02-03T14:55:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2030850.0 | 2014-02-03T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031735.0 | 2014-02-03T17:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041240.0 | 2014-02-04T12:40:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041455.0 | 2014-02-04T14:55:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2040850.0 | 2014-02-04T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041735.0 | 2014-02-04T17:35:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051240.0 | 2014-02-05T12:40:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051455.0 | 2014-02-05T14:55:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2050850.0 | 2014-02-05T08:50:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051735.0 | 2014-02-05T17:35:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061240.0 | 2014-02-06T12:40:00.000+0000 | 43.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061455.0 | 2014-02-06T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2060850.0 | 2014-02-06T08:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061735.0 | 2014-02-06T17:35:00.000+0000 | 34.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071240.0 | 2014-02-07T12:40:00.000+0000 | 88.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071455.0 | 2014-02-07T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2070850.0 | 2014-02-07T08:50:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071735.0 | 2014-02-07T17:35:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2081305.0 | 2014-02-08T13:05:00.000+0000 | -6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2081805.0 | 2014-02-08T18:05:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2080950.0 | 2014-02-08T09:50:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091235.0 | 2014-02-09T12:35:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091455.0 | 2014-02-09T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091735.0 | 2014-02-09T17:35:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101240.0 | 2014-02-10T12:40:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101455.0 | 2014-02-10T14:55:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2100850.0 | 2014-02-10T08:50:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101735.0 | 2014-02-10T17:35:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111240.0 | 2014-02-11T12:40:00.000+0000 | 145.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111455.0 | 2014-02-11T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2110850.0 | 2014-02-11T08:50:00.000+0000 | 96.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111735.0 | 2014-02-11T17:35:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121240.0 | 2014-02-12T12:40:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121455.0 | 2014-02-12T14:55:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2120850.0 | 2014-02-12T08:50:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121735.0 | 2014-02-12T17:35:00.000+0000 | 39.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131350.0 | 2014-02-13T13:50:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131610.0 | 2014-02-13T16:10:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2130810.0 | 2014-02-13T08:10:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131800.0 | 2014-02-13T18:00:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141610.0 | 2014-02-14T16:10:00.000+0000 | 37.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2140810.0 | 2014-02-14T08:10:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141350.0 | 2014-02-14T13:50:00.000+0000 | 46.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141800.0 | 2014-02-14T18:00:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
Building the Graph
Now that we've imported our data, we're going to need to build our graph. To do so we're going to do two things. We are going to build the structure of the vertices (or nodes) and we're going to build the structure of the edges. What's awesome about GraphFrames is that this process is incredibly simple.
- Rename IATA airport code to id in the Vertices Table
- Start and End airports to src and dst for the Edges Table (flights)
These are required naming conventions for vertices and edges in GraphFrames.
WARNING: If the graphframes package, required in the cell below, is not installed, follow the instructions here.
// Note, ensure you have already installed the GraphFrames spack-package
import org.apache.spark.sql.functions._
import org.graphframes._
// Create Vertices (airports) and Edges (flights)
val tripVertices = airports.withColumnRenamed("IATA", "id").distinct()
val tripEdges = departureDelays_geo.select("tripid", "delay", "src", "dst", "city_dst", "state_dst")
// Cache Vertices and Edges
tripEdges.cache()
tripVertices.cache()
import org.apache.spark.sql.functions._
import org.graphframes._
tripVertices: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, City: string ... 2 more fields]
tripEdges: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 4 more fields]
res5: tripVertices.type = [id: string, City: string ... 2 more fields]
// Vertices
// The vertices of our graph are the airports
display(tripVertices)
| id | City | State | Country |
|---|---|---|---|
| FAT | Fresno | CA | USA |
| CMH | Columbus | OH | USA |
| PHX | Phoenix | AZ | USA |
| PAH | Paducah | KY | USA |
| COS | Colorado Springs | CO | USA |
| RNO | Reno | NV | USA |
| MYR | Myrtle Beach | SC | USA |
| VLD | Valdosta | GA | USA |
| BPT | Beaumont | TX | USA |
| CAE | Columbia | SC | USA |
| PSC | Pasco | WA | USA |
| SRQ | Sarasota | FL | USA |
| LAX | Los Angeles | CA | USA |
| DAY | Dayton | OH | USA |
| AVP | Wilkes-Barre | PA | USA |
| MFR | Medford | OR | USA |
| JFK | New York | NY | USA |
| BNA | Nashville | TN | USA |
| CLT | Charlotte | NC | USA |
| LAS | Las Vegas | NV | USA |
| BDL | Hartford | CT | USA |
| ILG | Wilmington | DE | USA |
| ACT | Waco | TX | USA |
| ATW | Appleton | WI | USA |
| RHI | Rhinelander | WI | USA |
| PWM | Portland | ME | USA |
| SJT | San Angelo | TX | USA |
| APN | Alpena | MI | USA |
| GRB | Green Bay | WI | USA |
| CAK | Akron | OH | USA |
| LAN | Lansing | MI | USA |
| FLL | Fort Lauderdale | FL | USA |
| MSY | New Orleans | LA | USA |
| SAT | San Antonio | TX | USA |
| TPA | Tampa | FL | USA |
| COD | Cody | WY | USA |
| MOD | Modesto | CA | USA |
| GTR | Columbus | MS | USA |
| BTV | Burlington | VT | USA |
| RDD | Redding | CA | USA |
| HLN | Helena | MT | USA |
| CPR | Casper | WY | USA |
| FWA | Fort Wayne | IN | USA |
| IMT | Iron Mountain | MI | USA |
| DTW | Detroit | MI | USA |
| BZN | Bozeman | MT | USA |
| SBN | South Bend | IN | USA |
| SPS | Wichita Falls | TX | USA |
| BFL | Bakersfield | CA | USA |
| HOB | Hobbs | NM | USA |
| TVC | Traverse City | MI | USA |
| CLE | Cleveland | OH | USA |
| ABR | Aberdeen | SD | USA |
| CHS | Charleston | SC | USA |
| GUC | Gunnison | CO | USA |
| IND | Indianapolis | IN | USA |
| SDF | Louisville | KY | USA |
| SAN | San Diego | CA | USA |
| RSW | Fort Myers | FL | USA |
| BOS | Boston | MA | USA |
| TUL | Tulsa | OK | USA |
| AGS | Augusta | GA | USA |
| MOB | Mobile | AL | USA |
| TUS | Tucson | AZ | USA |
| KTN | Ketchikan | AK | USA |
| BTR | Baton Rouge | LA | USA |
| PNS | Pensacola | FL | USA |
| ABQ | Albuquerque | NM | USA |
| LGA | New York | NY | USA |
| DAL | Dallas | TX | USA |
| JNU | Juneau | AK | USA |
| MAF | Midland | TX | USA |
| RIC | Richmond | VA | USA |
| FAR | Fargo | ND | USA |
| MTJ | Montrose | CO | USA |
| AMA | Amarillo | TX | USA |
| ROC | Rochester | NY | USA |
| SHV | Shreveport | LA | USA |
| YAK | Yakutat | AK | USA |
| CSG | Columbus | GA | USA |
| ALO | Waterloo | IA | USA |
| DRO | Durango | CO | USA |
| CRP | Corpus Christi | TX | USA |
| FNT | Flint | MI | USA |
| GSO | Greensboro | NC | USA |
| LWS | Lewiston | ID | USA |
| TOL | Toledo | OH | USA |
| GTF | Great Falls | MT | USA |
| MKE | Milwaukee | WI | USA |
| RKS | Rock Springs | WY | USA |
| STL | St. Louis | MO | USA |
| MHT | Manchester | NH | USA |
| CRW | Charleston | WV | USA |
| SLC | Salt Lake City | UT | USA |
| ACV | Eureka | CA | USA |
| DFW | Dallas | TX | USA |
| OME | Nome | AK | USA |
| ORF | Norfolk | VA | USA |
| RDU | Raleigh | NC | USA |
| SYR | Syracuse | NY | USA |
| BQK | Brunswick | GA | USA |
| ROA | Roanoke | VA | USA |
| OKC | Oklahoma City | OK | USA |
| EYW | Key West | FL | USA |
| BOI | Boise | ID | USA |
| ABY | Albany | GA | USA |
| PIA | Peoria | IL | USA |
| GRI | Grand Island | NE | USA |
| PBI | West Palm Beach | FL | USA |
| FSM | Fort Smith | AR | USA |
| TYS | Knoxville | TN | USA |
| DAB | Daytona Beach | FL | USA |
| TTN | Trenton | NJ | USA |
| MDT | Harrisburg | PA | USA |
| AUS | Austin | TX | USA |
| DCA | Washington DC | null | USA |
| PIH | Pocatello | ID | USA |
| GCC | Gillette | WY | USA |
| BUR | Burbank | CA | USA |
| GRK | Killeen | TX | USA |
| LBB | Lubbock | TX | USA |
| JAN | Jackson | MS | USA |
| MSP | Minneapolis | MN | USA |
| SAF | Santa Fe | NM | USA |
| HPN | White Plains | NY | USA |
| DHN | Dothan | AL | USA |
| PSP | Palm Springs | CA | USA |
| INL | International Falls | MN | USA |
| CIC | Chico | CA | USA |
| EGE | Vail | CO | USA |
| CLD | Carlsbad | CA | USA |
| RST | Rochester | MN | USA |
| DEN | Denver | CO | USA |
| EUG | Eugene | OR | USA |
| LFT | Lafayette | LA | USA |
| PVD | Providence | RI | USA |
| DBQ | Dubuque | IA | USA |
| SAV | Savannah | GA | USA |
| SFO | San Francisco | CA | USA |
| JAX | Jacksonville | FL | USA |
| LIT | Little Rock | AR | USA |
| IDA | Idaho Falls | ID | USA |
| TYR | Tyler | TX | USA |
| ANC | Anchorage | AK | USA |
| HRL | Harlingen | TX | USA |
| LMT | Klamath Falls | OR | USA |
| PHF | Newport News | VA | USA |
| HOU | Houston | TX | USA |
| SPI | Springfield | IL | USA |
| MOT | Minot | ND | USA |
| BTM | Butte | MT | USA |
| SMF | Sacramento | CA | USA |
| HIB | Hibbing | MN | USA |
| ROW | Roswell | NM | USA |
| MBS | Saginaw | MI | USA |
| ILM | Wilmington | NC | USA |
| LGB | Long Beach | CA | USA |
| IAD | Washington DC | null | USA |
| ICT | Wichita | KS | USA |
| BGR | Bangor | ME | USA |
| ELP | El Paso | TX | USA |
| SUX | Sioux City | IA | USA |
| HSV | Huntsville | AL | USA |
| SIT | Sitka | AK | USA |
| CWA | Wausau | WI | USA |
| LCH | Lake Charles | LA | USA |
| CMI | Champaign | IL | USA |
| ELM | Elmira | NY | USA |
| CLL | College Station | TX | USA |
| VPS | Fort Walton Beach | FL | USA |
| MSN | Madison | WI | USA |
| MHK | Manhattan | KS | USA |
| OAJ | Jacksonville | NC | USA |
| EKO | Elko | NV | USA |
| FSD | Sioux Falls | SD | USA |
| EWR | Newark | NJ | USA |
| MEM | Memphis | TN | USA |
| ADQ | Kodiak | AK | USA |
| GGG | Longview | TX | USA |
| BLI | Bellingham | WA | USA |
| OMA | Omaha | NE | USA |
| ATL | Atlanta | GA | USA |
| SJC | San Jose | CA | USA |
| SBA | Santa Barbara | CA | USA |
| ONT | Ontario | CA | USA |
| EAU | Eau Claire | WI | USA |
| GPT | Gulfport | MS | USA |
| SUN | Sun Valley | ID | USA |
| CHO | Charlottesville | VA | USA |
| MSO | Missoula | MT | USA |
| CMX | Hancock | MI | USA |
| ORD | Chicago | IL | USA |
| EVV | Evansville | IN | USA |
| MLU | Monroe | LA | USA |
| LAW | Lawton | OK | USA |
| BRW | Barrow | AK | USA |
| SBP | San Luis Obispo | CA | USA |
| LIH | Lihue, Kauai | HI | USA |
| GJT | Grand Junction | CO | USA |
| FAI | Fairbanks | AK | USA |
| BIS | Bismarck | ND | USA |
| SNA | Orange County | CA | USA |
| LAR | Laramie | WY | USA |
| JLN | Joplin | MO | USA |
| LRD | Laredo | TX | USA |
| LEX | Lexington | KY | USA |
| SGU | St. George | UT | USA |
| MCI | Kansas City | MO | USA |
| AEX | Alexandria | LA | USA |
| ISN | Williston | ND | USA |
| TXK | Texarkana | AR | USA |
| BIL | Billings | MT | USA |
| CID | Cedar Rapids | IA | USA |
| PDX | Portland | OR | USA |
| ABE | Allentown | PA | USA |
| DIK | Dickinson | ND | USA |
| ART | Watertown | NY | USA |
| GCK | Garden City | KS | USA |
| BET | Bethel | AK | USA |
| AVL | Asheville | NC | USA |
| MCO | Orlando | FL | USA |
| GSP | Greenville | SC | USA |
| TWF | Twin Falls | ID | USA |
| MKG | Muskegon | MI | USA |
| FAY | Fayetteville | NC | USA |
| SCE | State College | PA | USA |
| EWN | New Bern | NC | USA |
| XNA | Fayetteville | AR | USA |
| MRY | Monterey | CA | USA |
| MLB | Melbourne | FL | USA |
| HNL | Honolulu, Oahu | HI | USA |
| CVG | Cincinnati | OH | USA |
| RAP | Rapid City | SD | USA |
| AZO | Kalamazoo | MI | USA |
| WRG | Wrangell | AK | USA |
| ISP | Islip | NY | USA |
| FLG | Flagstaff | AZ | USA |
| BHM | Birmingham | AL | USA |
| ALB | Albany | NY | USA |
| SEA | Seattle | WA | USA |
| GRR | Grand Rapids | MI | USA |
| CHA | Chattanooga | TN | USA |
| IAH | Houston | TX | USA |
| SMX | Santa Maria | CA | USA |
| MDW | Chicago | IL | USA |
| MQT | Marquette | MI | USA |
| LNK | Lincoln | NE | USA |
| RDM | Redmond | OR | USA |
| DLH | Duluth | MN | USA |
| DSM | Des Moines | IA | USA |
| OAK | Oakland | CA | USA |
| PHL | Philadelphia | PA | USA |
| FCA | Kalispell | MT | USA |
| MFE | McAllen | TX | USA |
| OGG | Kahului, Maui | HI | USA |
| YUM | Yuma | AZ | USA |
| BMI | Bloomington | IL | USA |
| GEG | Spokane | WA | USA |
| TLH | Tallahassee | FL | USA |
| LSE | La Crosse | WI | USA |
| MIA | Miami | FL | USA |
| BRO | Brownsville | TX | USA |
| JAC | Jackson Hole | WY | USA |
| CDV | Cordova | AK | USA |
| TRI | Tri-City Airport | TN | USA |
| SWF | Newburgh | NY | USA |
| MGM | Montgomery | AL | USA |
| BWI | Baltimore | MD | USA |
| SGF | Springfield | MO | USA |
| GFK | Grand Forks | ND | USA |
| GNV | Gainesville | FL | USA |
| OTH | North Bend | OR | USA |
| PIT | Pittsburgh | PA | USA |
| BJI | Bemidji | MN | USA |
| ASE | Aspen | CO | USA |
| BUF | Buffalo | NY | USA |
| COU | Columbia | MO | USA |
| ABI | Abilene | TX | USA |
| MLI | Moline | IL | USA |
// Edges
// The edges of our graph are the flights between airports
display(tripEdges)
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 1011111.0 | -5.0 | MSP | INL | International Falls | MN |
| 1021111.0 | 7.0 | MSP | INL | International Falls | MN |
| 1031111.0 | 0.0 | MSP | INL | International Falls | MN |
| 1041925.0 | 0.0 | MSP | INL | International Falls | MN |
| 1061115.0 | 33.0 | MSP | INL | International Falls | MN |
| 1071115.0 | 23.0 | MSP | INL | International Falls | MN |
| 1081115.0 | -9.0 | MSP | INL | International Falls | MN |
| 1091115.0 | 11.0 | MSP | INL | International Falls | MN |
| 1101115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1112015.0 | -7.0 | MSP | INL | International Falls | MN |
| 1121925.0 | -5.0 | MSP | INL | International Falls | MN |
| 1131115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1141115.0 | -6.0 | MSP | INL | International Falls | MN |
| 1151115.0 | -7.0 | MSP | INL | International Falls | MN |
| 1161115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1171115.0 | 4.0 | MSP | INL | International Falls | MN |
| 1182015.0 | -5.0 | MSP | INL | International Falls | MN |
| 1191925.0 | -7.0 | MSP | INL | International Falls | MN |
| 1201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 1211115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1221115.0 | -4.0 | MSP | INL | International Falls | MN |
| 1231115.0 | -4.0 | MSP | INL | International Falls | MN |
| 1241115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1252015.0 | -12.0 | MSP | INL | International Falls | MN |
| 1261925.0 | -5.0 | MSP | INL | International Falls | MN |
| 1271115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1281115.0 | -8.0 | MSP | INL | International Falls | MN |
| 1291115.0 | -2.0 | MSP | INL | International Falls | MN |
| 1301115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1311115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2012015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2022015.0 | 0.0 | MSP | INL | International Falls | MN |
| 2031115.0 | -7.0 | MSP | INL | International Falls | MN |
| 2041115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2051115.0 | -4.0 | MSP | INL | International Falls | MN |
| 2061115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2071115.0 | -15.0 | MSP | INL | International Falls | MN |
| 2082015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2091925.0 | 1.0 | MSP | INL | International Falls | MN |
| 2101115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2111115.0 | -7.0 | MSP | INL | International Falls | MN |
| 2121115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2131115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2141115.0 | -11.0 | MSP | INL | International Falls | MN |
| 2152015.0 | 16.0 | MSP | INL | International Falls | MN |
| 2161925.0 | 169.0 | MSP | INL | International Falls | MN |
| 2171115.0 | 27.0 | MSP | INL | International Falls | MN |
| 2181115.0 | 96.0 | MSP | INL | International Falls | MN |
| 2191115.0 | -9.0 | MSP | INL | International Falls | MN |
| 2201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2211115.0 | -4.0 | MSP | INL | International Falls | MN |
| 2222015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2231925.0 | -3.0 | MSP | INL | International Falls | MN |
| 2241115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2251115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2261115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2271115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2281115.0 | 5.0 | MSP | INL | International Falls | MN |
| 3012015.0 | -4.0 | MSP | INL | International Falls | MN |
| 3022000.0 | 0.0 | MSP | INL | International Falls | MN |
| 3031115.0 | 17.0 | MSP | INL | International Falls | MN |
| 3041115.0 | 0.0 | MSP | INL | International Falls | MN |
| 3051115.0 | -7.0 | MSP | INL | International Falls | MN |
| 3061115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3071115.0 | -10.0 | MSP | INL | International Falls | MN |
| 3082000.0 | -11.0 | MSP | INL | International Falls | MN |
| 3092000.0 | -9.0 | MSP | INL | International Falls | MN |
| 3101115.0 | -10.0 | MSP | INL | International Falls | MN |
| 3111115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3121115.0 | -6.0 | MSP | INL | International Falls | MN |
| 3131115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3141115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3152000.0 | -11.0 | MSP | INL | International Falls | MN |
| 3162000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3171115.0 | 25.0 | MSP | INL | International Falls | MN |
| 3181115.0 | 2.0 | MSP | INL | International Falls | MN |
| 3191115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 3211115.0 | 0.0 | MSP | INL | International Falls | MN |
| 3222000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3232000.0 | -9.0 | MSP | INL | International Falls | MN |
| 3241115.0 | -9.0 | MSP | INL | International Falls | MN |
| 3251115.0 | -4.0 | MSP | INL | International Falls | MN |
| 3261115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3271115.0 | 9.0 | MSP | INL | International Falls | MN |
| 3281115.0 | -7.0 | MSP | INL | International Falls | MN |
| 3292000.0 | -19.0 | MSP | INL | International Falls | MN |
| 3302000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3311115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2011230.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2010719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2021230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2020709.0 | 59.0 | EWR | MSY | New Orleans | LA |
| 2021654.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2030719.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 2031659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2031230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2032043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2041230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 2040719.0 | 168.0 | EWR | MSY | New Orleans | LA |
| 2041730.0 | 88.0 | EWR | MSY | New Orleans | LA |
| 2042043.0 | 106.0 | EWR | MSY | New Orleans | LA |
| 2051659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2050719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2050929.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2052043.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2060719.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2061659.0 | 82.0 | EWR | MSY | New Orleans | LA |
| 2061230.0 | 61.0 | EWR | MSY | New Orleans | LA |
| 2062043.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2070719.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2071659.0 | 19.0 | EWR | MSY | New Orleans | LA |
| 2071230.0 | 27.0 | EWR | MSY | New Orleans | LA |
| 2072048.0 | 47.0 | EWR | MSY | New Orleans | LA |
| 2081230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2080719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2091229.0 | 95.0 | EWR | MSY | New Orleans | LA |
| 2091654.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2090709.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2092043.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2100719.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 2101659.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 2101230.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2102043.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2111230.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2110719.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2111730.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 2112043.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2120719.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 2121230.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2120929.0 | 89.0 | EWR | MSY | New Orleans | LA |
| 2122043.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2130738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2132041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2140729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2142041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2151206.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2150659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2160705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2170729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2172041.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2180738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2182041.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2190727.0 | 15.0 | EWR | MSY | New Orleans | LA |
| 2200738.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2202041.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 2210729.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2212041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2221206.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2220659.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2232041.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2230705.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2240729.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2242041.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2250738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2260727.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2262041.0 | 174.0 | EWR | MSY | New Orleans | LA |
| 2270738.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2272041.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2280729.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 2282041.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 2281000.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 2051230.0 | 216.0 | EWR | MSY | New Orleans | LA |
| 2131536.0 | 273.0 | EWR | MSY | New Orleans | LA |
| 2141536.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2151902.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 2151536.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 2162041.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2161536.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2171536.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 2181536.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 2191536.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 2201536.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2211536.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 2221900.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2221536.0 | 65.0 | EWR | MSY | New Orleans | LA |
| 2231536.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2241536.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2251536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2261536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2271536.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2281536.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2010730.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2021815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2031815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2041815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2051815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2061815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2071815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2080730.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2091815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2101815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2111815.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 2121815.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 2131805.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2141635.0 | 52.0 | EWR | MSY | New Orleans | LA |
| 2150730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2161635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2171635.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2181635.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2191635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2201635.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2211635.0 | 292.0 | EWR | MSY | New Orleans | LA |
| 2220730.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 2231635.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2241635.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2251635.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2261635.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2271635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2281635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3011206.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3010659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3022041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3020705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3030729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3032041.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 3040738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3050727.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3052041.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 3060705.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3061252.0 | 67.0 | EWR | MSY | New Orleans | LA |
| 3062100.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 3070705.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3071252.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3072100.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 3080705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3081250.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3091255.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 3092059.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3100705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3101252.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 3102059.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3112059.0 | 181.0 | EWR | MSY | New Orleans | LA |
| 3111252.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3122059.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3121252.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 3130705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3131252.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3132059.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3140705.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 3141252.0 | 39.0 | EWR | MSY | New Orleans | LA |
| 3142059.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3150700.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3151250.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 3161255.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3162059.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3170705.0 | -11.0 | EWR | MSY | New Orleans | LA |
| 3171252.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3172059.0 | 132.0 | EWR | MSY | New Orleans | LA |
| 3182059.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3181252.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3192059.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3191252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3200705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3201252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3202059.0 | 77.0 | EWR | MSY | New Orleans | LA |
| 3210705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3211252.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3212059.0 | 11.0 | EWR | MSY | New Orleans | LA |
| 3220705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3221250.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3221600.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3231255.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3240705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3241252.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3242059.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 3252059.0 | 121.0 | EWR | MSY | New Orleans | LA |
| 3251252.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3262059.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 3261252.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3270705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3271252.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3272059.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3280705.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3281252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3282059.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3291250.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3290705.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3291600.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3301255.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3302059.0 | 166.0 | EWR | MSY | New Orleans | LA |
| 3310705.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3311252.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 3312059.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 3011902.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3011536.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3021536.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3031536.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 3041536.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3051536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3090715.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3110705.0 | -11.0 | EWR | MSY | New Orleans | LA |
| 3120659.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3160715.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3180705.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3190659.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3230715.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3250705.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3260659.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 3300715.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3010730.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3021635.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3031635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3041635.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 3051635.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3061635.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3071635.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3080730.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3091825.0 | 45.0 | EWR | MSY | New Orleans | LA |
| 3101825.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3111825.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3121825.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3131825.0 | 123.0 | EWR | MSY | New Orleans | LA |
| 3141825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3150730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3161825.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 3171825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3181825.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3191825.0 | 223.0 | EWR | MSY | New Orleans | LA |
| 3201825.0 | 178.0 | EWR | MSY | New Orleans | LA |
| 3211825.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3220730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3231825.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3241825.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3251825.0 | 222.0 | EWR | MSY | New Orleans | LA |
| 3261825.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 3271825.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3281825.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 3290730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3301825.0 | 139.0 | EWR | MSY | New Orleans | LA |
| 3311825.0 | 25.0 | EWR | MSY | New Orleans | LA |
| 1020705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1030705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1040655.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1050703.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 1060705.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 1071230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 1070719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1071730.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 1072043.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1080719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1081659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1081230.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 1082043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1090719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1091659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1091230.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1092043.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 1100719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1101659.0 | 244.0 | EWR | MSY | New Orleans | LA |
| 1101230.0 | 110.0 | EWR | MSY | New Orleans | LA |
| 1102043.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1111230.0 | 87.0 | EWR | MSY | New Orleans | LA |
| 1110719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1121654.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1121230.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 1120709.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1122043.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1130719.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 1131659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1131230.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1132043.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 1141230.0 | 30.0 | EWR | MSY | New Orleans | LA |
| 1140719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1141730.0 | 69.0 | EWR | MSY | New Orleans | LA |
| 1142043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1150719.0 | 42.0 | EWR | MSY | New Orleans | LA |
| 1151659.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1151230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1152043.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1160719.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1161659.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 1161230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 1162043.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1171659.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 1170719.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 1172043.0 | 54.0 | EWR | MSY | New Orleans | LA |
| 1181230.0 | 20.0 | EWR | MSY | New Orleans | LA |
| 1180719.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1191654.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1191230.0 | 29.0 | EWR | MSY | New Orleans | LA |
| 1190709.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1192043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1200719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1202043.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1211230.0 | 102.0 | EWR | MSY | New Orleans | LA |
| 1210719.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1211730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1212043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1220719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1221659.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 1221230.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1222043.0 | 70.0 | EWR | MSY | New Orleans | LA |
| 1230719.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1231659.0 | 94.0 | EWR | MSY | New Orleans | LA |
| 1231230.0 | 111.0 | EWR | MSY | New Orleans | LA |
| 1232043.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 1240719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1241659.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1241230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1242043.0 | 56.0 | EWR | MSY | New Orleans | LA |
| 1251230.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1250719.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 1261654.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 1261230.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1260709.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1262043.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 1271659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1270719.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1281230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1280719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1281730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1282043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1291659.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 1290719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1292043.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 1300719.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1301659.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 1301230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1302043.0 | 93.0 | EWR | MSY | New Orleans | LA |
| 1310719.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 1311659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1311230.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1312043.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 1171230.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1201250.0 | -12.0 | EWR | MSY | New Orleans | LA |
| 1291230.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1011815.0 | 125.0 | EWR | MSY | New Orleans | LA |
| 1021815.0 | 33.0 | EWR | MSY | New Orleans | LA |
| 1031815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1040755.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1051815.0 | 172.0 | EWR | MSY | New Orleans | LA |
| 1061815.0 | 151.0 | EWR | MSY | New Orleans | LA |
| 1071815.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1081815.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1091815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1101815.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 1110730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1121815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1131815.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1141815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1151815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1161815.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1171815.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1180730.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1191815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1211815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1221815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1231815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1241815.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1250730.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1261815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1271815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1281815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1291815.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1301815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1311815.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2011055.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 2021025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 2031025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 2031750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 2041025.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 2041750.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2051025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2051750.0 | 29.0 | LAS | MSY | New Orleans | LA |
| 2061025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2061750.0 | 48.0 | LAS | MSY | New Orleans | LA |
| 2071025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2071750.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 2081055.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2091025.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2091750.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 2101025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 2101750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 2111025.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2111750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 2121025.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2121750.0 | 158.0 | LAS | MSY | New Orleans | LA |
| 2131855.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2131215.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 2141855.0 | 22.0 | LAS | MSY | New Orleans | LA |
| 2141215.0 | 18.0 | LAS | MSY | New Orleans | LA |
| 2150935.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2151635.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2161855.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2161215.0 | 17.0 | LAS | MSY | New Orleans | LA |
| 2171855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2171215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2181855.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2181215.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2191855.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 2191215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2201855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2201215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 2211855.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2211215.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2220935.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 2221635.0 | 133.0 | LAS | MSY | New Orleans | LA |
| 2231855.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2231215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2241855.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2241215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2251855.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 2251215.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2261855.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2261215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2271855.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2271215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2281855.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 2281215.0 | 94.0 | LAS | MSY | New Orleans | LA |
| 3010935.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3011635.0 | 39.0 | LAS | MSY | New Orleans | LA |
| 3021855.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3021215.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3031855.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 3031215.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 3041855.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3041215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 3051855.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 3051215.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 3061855.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 3061215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3071855.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3071215.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3081940.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 3080830.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 3091905.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3090840.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 3101905.0 | 49.0 | LAS | MSY | New Orleans | LA |
| 3100840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3111905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3110840.0 | 84.0 | LAS | MSY | New Orleans | LA |
| 3121905.0 | 26.0 | LAS | MSY | New Orleans | LA |
| 3120840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3131905.0 | 37.0 | LAS | MSY | New Orleans | LA |
| 3130840.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 3141905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3140840.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3150830.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3151940.0 | 95.0 | LAS | MSY | New Orleans | LA |
| 3161905.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 3160840.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3171905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3170840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3181905.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 3180840.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 3191905.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3190840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3201905.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3200840.0 | 68.0 | LAS | MSY | New Orleans | LA |
| 3211905.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3210840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3220830.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 3221940.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 3231905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3230840.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3241905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3240840.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3251905.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3250840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3261905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3260840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3271905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3270840.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3281905.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3280840.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 3290830.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3291940.0 | 231.0 | LAS | MSY | New Orleans | LA |
| 3301905.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 3300840.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 3311905.0 | 19.0 | LAS | MSY | New Orleans | LA |
| 3310840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1010850.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1011755.0 | 160.0 | LAS | MSY | New Orleans | LA |
| 1021805.0 | 138.0 | LAS | MSY | New Orleans | LA |
| 1020905.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1031805.0 | 154.0 | LAS | MSY | New Orleans | LA |
| 1030905.0 | 179.0 | LAS | MSY | New Orleans | LA |
| 1041655.0 | 113.0 | LAS | MSY | New Orleans | LA |
| 1040900.0 | 56.0 | LAS | MSY | New Orleans | LA |
| 1050905.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1051805.0 | 53.0 | LAS | MSY | New Orleans | LA |
| 1061755.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 1060905.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 1071025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1071750.0 | 302.0 | LAS | MSY | New Orleans | LA |
| 1081025.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 1081750.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 1091025.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1091750.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 1101025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1101750.0 | 92.0 | LAS | MSY | New Orleans | LA |
| 1111055.0 | 31.0 | LAS | MSY | New Orleans | LA |
| 1121025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1121750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1131025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1131750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1141025.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1141750.0 | 127.0 | LAS | MSY | New Orleans | LA |
| 1151025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1151750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1161025.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1161750.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1171025.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 1171750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1181055.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1191025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1191750.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1201025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1201750.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 1211025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1211750.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 1221025.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1221750.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1231025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1231750.0 | 30.0 | LAS | MSY | New Orleans | LA |
| 1241025.0 | 43.0 | LAS | MSY | New Orleans | LA |
| 1241750.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1251055.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1261025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1261750.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 1271025.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1271750.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 1281025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1281750.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1291025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1291750.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1301025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1301750.0 | 35.0 | LAS | MSY | New Orleans | LA |
| 1311025.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 1311750.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 2011530.0 | -3.0 | MCI | MSY | New Orleans | LA |
| 2021105.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 2031105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2041105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 2051105.0 | 6.0 | MCI | MSY | New Orleans | LA |
| 2061105.0 | 40.0 | MCI | MSY | New Orleans | LA |
| 2071105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2081530.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2091105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2101105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2111105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2121105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2130800.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 2140830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2150750.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2160930.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2170830.0 | 10.0 | MCI | MSY | New Orleans | LA |
| 2180830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2190830.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 2200830.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 2210830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2220750.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 2230930.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2240830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2250830.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 2260830.0 | 251.0 | MCI | MSY | New Orleans | LA |
| 2270830.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2280830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3010750.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3020930.0 | 35.0 | MCI | MSY | New Orleans | LA |
| 3030830.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3040830.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 3050830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 3060830.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3070830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3081610.0 | 93.0 | MCI | MSY | New Orleans | LA |
| 3090950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3100950.0 | 8.0 | MCI | MSY | New Orleans | LA |
| 3110950.0 | 36.0 | MCI | MSY | New Orleans | LA |
| 3120950.0 | 68.0 | MCI | MSY | New Orleans | LA |
| 3130950.0 | 13.0 | MCI | MSY | New Orleans | LA |
| 3140950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3151610.0 | 16.0 | MCI | MSY | New Orleans | LA |
| 3160950.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 3170950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3180950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3190950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3200950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 3210950.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 3221610.0 | 38.0 | MCI | MSY | New Orleans | LA |
| 3230950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3240950.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3250950.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 3260950.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 3270950.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3280950.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3291610.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3300950.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3310950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1011635.0 | -7.0 | MCI | MSY | New Orleans | LA |
| 1021635.0 | 96.0 | MCI | MSY | New Orleans | LA |
| 1031635.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1041205.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1051635.0 | 60.0 | MCI | MSY | New Orleans | LA |
| 1061635.0 | -7.0 | MCI | MSY | New Orleans | LA |
| 1071105.0 | 14.0 | MCI | MSY | New Orleans | LA |
| 1081105.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 1091105.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1101105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 1111530.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1121105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1131105.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 1141105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1151105.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 1161105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1171105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 1181530.0 | 48.0 | MCI | MSY | New Orleans | LA |
| 1191105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1201105.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 1211105.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 1221105.0 | 19.0 | MCI | MSY | New Orleans | LA |
| 1231105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 1241105.0 | 72.0 | MCI | MSY | New Orleans | LA |
| 1251530.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 1261105.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 1271105.0 | -6.0 | MCI | MSY | New Orleans | LA |
| 1281105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1291105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1301105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1311105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3011530.0 | 72.0 | BNA | MSY | New Orleans | LA |
| 3010850.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 3011245.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3021610.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 3021350.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3021800.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3031610.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3030810.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3031350.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3031800.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3041610.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3040810.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3041350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3041800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3051610.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3050810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3051350.0 | 48.0 | BNA | MSY | New Orleans | LA |
| 3051800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3061610.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 3060810.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3061350.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3061800.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3071610.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3070810.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3071350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3071800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3081540.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3081335.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3080945.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3091620.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3091820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3091420.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3100820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3101420.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3101820.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3101620.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3110820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3111420.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3111820.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3111620.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3120820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3121420.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3121820.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3121620.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 3130820.0 | 126.0 | BNA | MSY | New Orleans | LA |
| 3131420.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3131820.0 | 55.0 | BNA | MSY | New Orleans | LA |
| 3131620.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3140820.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3141420.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3141820.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3141620.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3151335.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3150945.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3151540.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3161620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3161820.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 3161420.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3170820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3171420.0 | 112.0 | BNA | MSY | New Orleans | LA |
| 3171820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3171620.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3180820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3181420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3181820.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3181620.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3190820.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3191420.0 | 54.0 | BNA | MSY | New Orleans | LA |
| 3191820.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3191620.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3200820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3201420.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 3201820.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 3201620.0 | 79.0 | BNA | MSY | New Orleans | LA |
| 3210820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3211420.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3211820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3211620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3221335.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3220945.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3221540.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3231620.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3231820.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 3231420.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3240820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3241420.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3241820.0 | 44.0 | BNA | MSY | New Orleans | LA |
| 3241620.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3250820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3251420.0 | 70.0 | BNA | MSY | New Orleans | LA |
| 3251820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3251620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3260820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3261420.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 3261820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3261620.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3270820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3271420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3271820.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3271620.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 3280820.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3281420.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 3281820.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 3281620.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3291335.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3290945.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 3291540.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3301620.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3301820.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3301420.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3310820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3311420.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 3311820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3311620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 1010800.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1011210.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1011835.0 | 53.0 | BNA | MSY | New Orleans | LA |
| 1021215.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 1021835.0 | 200.0 | BNA | MSY | New Orleans | LA |
| 1020800.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 1031215.0 | 185.0 | BNA | MSY | New Orleans | LA |
| 1031835.0 | 226.0 | BNA | MSY | New Orleans | LA |
| 1030800.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1041420.0 | 174.0 | BNA | MSY | New Orleans | LA |
| 1040835.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1051215.0 | 85.0 | BNA | MSY | New Orleans | LA |
| 1051835.0 | 283.0 | BNA | MSY | New Orleans | LA |
| 1050800.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1061215.0 | 150.0 | BNA | MSY | New Orleans | LA |
| 1061835.0 | 133.0 | BNA | MSY | New Orleans | LA |
| 1060800.0 | 102.0 | BNA | MSY | New Orleans | LA |
| 1071240.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1071455.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1070905.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1071735.0 | 131.0 | BNA | MSY | New Orleans | LA |
| 1081240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1081455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1080905.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1081735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 1091240.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 1091455.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 1090905.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1091735.0 | 28.0 | BNA | MSY | New Orleans | LA |
| 1101240.0 | 50.0 | BNA | MSY | New Orleans | LA |
| 1101455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 1100905.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1101735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1111305.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1111805.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1110950.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1121235.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1121455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1121735.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1131240.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1131455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1130850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1131735.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 1141240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1141455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1140850.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 1141735.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1151240.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1151455.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 1150850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1151735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1161240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161455.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1160850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1161735.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 1171240.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 1171455.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1170850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1171735.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 1181305.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1181805.0 | 42.0 | BNA | MSY | New Orleans | LA |
| 1180950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191235.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 1191455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191735.0 | 64.0 | BNA | MSY | New Orleans | LA |
| 1201240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1201455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1200850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1201735.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1211240.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 1211455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1210850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1211735.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1221240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1221455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1220850.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 1221735.0 | 118.0 | BNA | MSY | New Orleans | LA |
| 1231240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1231455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1230850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1231735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1241240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1241455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1240850.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1241735.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1251305.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 1251805.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1250950.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1261235.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1261455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1261735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1271240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1271455.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1270850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1271735.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1281240.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1281455.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1280850.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1281735.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1291240.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1291455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1290850.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1291735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1301240.0 | 38.0 | BNA | MSY | New Orleans | LA |
| 1301455.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 1300850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1301735.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1311240.0 | 31.0 | BNA | MSY | New Orleans | LA |
| 1311455.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 1310850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1311735.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2011305.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2011805.0 | -6.0 | BNA | MSY | New Orleans | LA |
| 2010950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 2021455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2021235.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2021650.0 | 77.0 | BNA | MSY | New Orleans | LA |
| 2031240.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 2031455.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2030850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2031735.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 2041240.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 2041455.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2040850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2041735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2051240.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 2051455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2050850.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 2051735.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2061240.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 2061455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2060850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2061735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 2071240.0 | 88.0 | BNA | MSY | New Orleans | LA |
| 2071455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2070850.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2071735.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 2081305.0 | -6.0 | BNA | MSY | New Orleans | LA |
| 2081805.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 2080950.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 2091235.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2091455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2091735.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2101240.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2101455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2100850.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2101735.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2111240.0 | 145.0 | BNA | MSY | New Orleans | LA |
| 2111455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2110850.0 | 96.0 | BNA | MSY | New Orleans | LA |
| 2111735.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2121240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2121455.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 2120850.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2121735.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 2131350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2131610.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2130810.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 2131800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2141610.0 | 37.0 | BNA | MSY | New Orleans | LA |
| 2140810.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 2141350.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2141800.0 | 21.0 | BNA | MSY | New Orleans | LA |
// Build `tripGraph` GraphFrame
// This GraphFrame builds up on the vertices and edges based on our trips (flights)
val tripGraph = GraphFrame(tripVertices, tripEdges)
println(tripGraph)
// Build `tripGraphPrime` GraphFrame
// This graphframe contains a smaller subset of data to make it easier to display motifs and subgraphs (below)
val tripEdgesPrime = departureDelays_geo.select("tripid", "delay", "src", "dst")
val tripGraphPrime = GraphFrame(tripVertices, tripEdgesPrime)
GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripGraph: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripEdgesPrime: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 2 more fields]
tripGraphPrime: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 2 more fields])
Simple Queries
Let's start with a set of simple graph queries to understand flight performance and departure delays
println(s"Airports: ${tripGraph.vertices.count()}")
println(s"Trips: ${tripGraph.edges.count()}")
Airports: 279
Trips: 1361141
// Finding the longest Delay
val longestDelay = tripGraph.edges.groupBy().max("delay")
display(longestDelay)
| max(delay) |
|---|
| 1642.0 |
1642.0/60.0
res13: Double = 27.366666666666667
// Determining number of on-time / early flights vs. delayed flights
println(s"On-time / Early Flights: ${tripGraph.edges.filter("delay <= 0").count()}")
println(s"Delayed Flights: ${tripGraph.edges.filter("delay > 0").count()}")
On-time / Early Flights: 780469
Delayed Flights: 580672
What flights departing SFO are most likely to have significant delays
Note, delay can be <= 0 meaning the flight left on time or early
//tripGraph.createOrReplaceTempView("tripgraph")
val sfoDelayedTrips = tripGraph.edges.
filter("src = 'SFO' and delay > 0").
groupBy("src", "dst").
avg("delay").
sort(desc("avg(delay)"))
sfoDelayedTrips: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
display(sfoDelayedTrips)
| src | dst | avg(delay) |
|---|---|---|
| SFO | OKC | 59.073170731707314 |
| SFO | JAC | 57.13333333333333 |
| SFO | COS | 53.976190476190474 |
| SFO | OTH | 48.09090909090909 |
| SFO | SAT | 47.625 |
| SFO | MOD | 46.80952380952381 |
| SFO | SUN | 46.723404255319146 |
| SFO | CIC | 46.72164948453608 |
| SFO | ABQ | 44.8125 |
| SFO | ASE | 44.285714285714285 |
| SFO | PIT | 43.875 |
| SFO | MIA | 43.81730769230769 |
| SFO | FAT | 43.23972602739726 |
| SFO | MFR | 43.11848341232228 |
| SFO | SBP | 43.09770114942529 |
| SFO | MSP | 42.766917293233085 |
| SFO | BOI | 42.65482233502538 |
| SFO | RDM | 41.98823529411764 |
| SFO | AUS | 41.690677966101696 |
| SFO | SLC | 41.407272727272726 |
| SFO | JFK | 41.01379310344828 |
| SFO | PSP | 40.909909909909906 |
| SFO | PHX | 40.67272727272727 |
| SFO | MRY | 40.61764705882353 |
| SFO | ACV | 40.3728813559322 |
| SFO | LAS | 40.107602339181284 |
| SFO | TUS | 39.853658536585364 |
| SFO | SAN | 38.97361809045226 |
| SFO | SBA | 38.758620689655174 |
| SFO | BFL | 38.51136363636363 |
| SFO | RDU | 38.170731707317074 |
| SFO | STL | 38.13513513513514 |
| SFO | IND | 38.114285714285714 |
| SFO | EUG | 37.573913043478264 |
| SFO | RNO | 36.81372549019608 |
| SFO | BUR | 36.75675675675676 |
| SFO | LGB | 36.752941176470586 |
| SFO | HNL | 36.25367647058823 |
| SFO | LAX | 36.165543071161046 |
| SFO | RDD | 36.11009174311926 |
| SFO | MSY | 35.421052631578945 |
| SFO | SMF | 34.936 |
| SFO | MDW | 34.824742268041234 |
| SFO | FLL | 34.76842105263158 |
| SFO | SEA | 34.68854961832061 |
| SFO | MCI | 34.68571428571428 |
| SFO | DFW | 34.36642599277978 |
| SFO | OGG | 34.171875 |
| SFO | PDX | 34.14430894308943 |
| SFO | ORD | 33.991130820399114 |
| SFO | LIH | 32.93023255813954 |
| SFO | DEN | 32.861491628614914 |
| SFO | PSC | 32.604651162790695 |
| SFO | PHL | 32.440677966101696 |
| SFO | BWI | 31.70212765957447 |
| SFO | ONT | 31.49079754601227 |
| SFO | SNA | 31.18426103646833 |
| SFO | MCO | 31.03488372093023 |
| SFO | MKE | 31.03448275862069 |
| SFO | CLE | 30.979591836734695 |
| SFO | EWR | 30.354285714285716 |
| SFO | BOS | 29.623471882640587 |
| SFO | LMT | 29.233333333333334 |
| SFO | DTW | 28.34722222222222 |
| SFO | IAH | 28.322105263157894 |
| SFO | CVG | 27.03125 |
| SFO | ATL | 26.84860557768924 |
| SFO | IAD | 26.125964010282775 |
| SFO | ANC | 25.5 |
| SFO | BZN | 23.964285714285715 |
| SFO | CLT | 22.636363636363637 |
| SFO | DCA | 21.896103896103895 |
// After displaying tripDelays, use Plot Options to set `state_dst` as a Key.
val tripDelays = tripGraph.edges.filter($"delay" > 0)
display(tripDelays)
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 1021111.0 | 7.0 | MSP | INL | International Falls | MN |
| 1061115.0 | 33.0 | MSP | INL | International Falls | MN |
| 1071115.0 | 23.0 | MSP | INL | International Falls | MN |
| 1091115.0 | 11.0 | MSP | INL | International Falls | MN |
| 1171115.0 | 4.0 | MSP | INL | International Falls | MN |
| 2091925.0 | 1.0 | MSP | INL | International Falls | MN |
| 2152015.0 | 16.0 | MSP | INL | International Falls | MN |
| 2161925.0 | 169.0 | MSP | INL | International Falls | MN |
| 2171115.0 | 27.0 | MSP | INL | International Falls | MN |
| 2181115.0 | 96.0 | MSP | INL | International Falls | MN |
| 2281115.0 | 5.0 | MSP | INL | International Falls | MN |
| 3031115.0 | 17.0 | MSP | INL | International Falls | MN |
| 3171115.0 | 25.0 | MSP | INL | International Falls | MN |
| 3181115.0 | 2.0 | MSP | INL | International Falls | MN |
| 3271115.0 | 9.0 | MSP | INL | International Falls | MN |
| 2020709.0 | 59.0 | EWR | MSY | New Orleans | LA |
| 2021654.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2041230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 2040719.0 | 168.0 | EWR | MSY | New Orleans | LA |
| 2041730.0 | 88.0 | EWR | MSY | New Orleans | LA |
| 2042043.0 | 106.0 | EWR | MSY | New Orleans | LA |
| 2052043.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2061659.0 | 82.0 | EWR | MSY | New Orleans | LA |
| 2061230.0 | 61.0 | EWR | MSY | New Orleans | LA |
| 2062043.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2070719.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2071659.0 | 19.0 | EWR | MSY | New Orleans | LA |
| 2071230.0 | 27.0 | EWR | MSY | New Orleans | LA |
| 2072048.0 | 47.0 | EWR | MSY | New Orleans | LA |
| 2091229.0 | 95.0 | EWR | MSY | New Orleans | LA |
| 2092043.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2100719.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 2101659.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 2111230.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2110719.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2120719.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 2120929.0 | 89.0 | EWR | MSY | New Orleans | LA |
| 2122043.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2182041.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2190727.0 | 15.0 | EWR | MSY | New Orleans | LA |
| 2202041.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 2220659.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2232041.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2240729.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2260727.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2262041.0 | 174.0 | EWR | MSY | New Orleans | LA |
| 2270738.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2272041.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2280729.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 2282041.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 2281000.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 2051230.0 | 216.0 | EWR | MSY | New Orleans | LA |
| 2131536.0 | 273.0 | EWR | MSY | New Orleans | LA |
| 2141536.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2151902.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 2151536.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 2161536.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2171536.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 2181536.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 2211536.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 2221536.0 | 65.0 | EWR | MSY | New Orleans | LA |
| 2281536.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2111815.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 2121815.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 2141635.0 | 52.0 | EWR | MSY | New Orleans | LA |
| 2161635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2171635.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2191635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2211635.0 | 292.0 | EWR | MSY | New Orleans | LA |
| 2220730.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 2251635.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2261635.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3032041.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 3052041.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 3061252.0 | 67.0 | EWR | MSY | New Orleans | LA |
| 3062100.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 3072100.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 3081250.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3112059.0 | 181.0 | EWR | MSY | New Orleans | LA |
| 3122059.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3121252.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 3140705.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 3141252.0 | 39.0 | EWR | MSY | New Orleans | LA |
| 3150700.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3151250.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 3171252.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3172059.0 | 132.0 | EWR | MSY | New Orleans | LA |
| 3181252.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3192059.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3202059.0 | 77.0 | EWR | MSY | New Orleans | LA |
| 3211252.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3212059.0 | 11.0 | EWR | MSY | New Orleans | LA |
| 3231255.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3252059.0 | 121.0 | EWR | MSY | New Orleans | LA |
| 3262059.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 3291600.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3301255.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3302059.0 | 166.0 | EWR | MSY | New Orleans | LA |
| 3311252.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 3312059.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 3011902.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3021536.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3090715.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3260659.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 3300715.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3021635.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3041635.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 3071635.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3091825.0 | 45.0 | EWR | MSY | New Orleans | LA |
| 3101825.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3111825.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3131825.0 | 123.0 | EWR | MSY | New Orleans | LA |
| 3141825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3161825.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 3171825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3191825.0 | 223.0 | EWR | MSY | New Orleans | LA |
| 3201825.0 | 178.0 | EWR | MSY | New Orleans | LA |
| 3251825.0 | 222.0 | EWR | MSY | New Orleans | LA |
| 3261825.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 3281825.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 3301825.0 | 139.0 | EWR | MSY | New Orleans | LA |
| 3311825.0 | 25.0 | EWR | MSY | New Orleans | LA |
| 1050703.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 1060705.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 1071230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 1071730.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 1072043.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1081230.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 1082043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1092043.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 1101659.0 | 244.0 | EWR | MSY | New Orleans | LA |
| 1101230.0 | 110.0 | EWR | MSY | New Orleans | LA |
| 1102043.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1111230.0 | 87.0 | EWR | MSY | New Orleans | LA |
| 1121230.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 1131230.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1132043.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 1141230.0 | 30.0 | EWR | MSY | New Orleans | LA |
| 1141730.0 | 69.0 | EWR | MSY | New Orleans | LA |
| 1150719.0 | 42.0 | EWR | MSY | New Orleans | LA |
| 1151659.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1152043.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1162043.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1171659.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 1172043.0 | 54.0 | EWR | MSY | New Orleans | LA |
| 1181230.0 | 20.0 | EWR | MSY | New Orleans | LA |
| 1191230.0 | 29.0 | EWR | MSY | New Orleans | LA |
| 1192043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1202043.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1211230.0 | 102.0 | EWR | MSY | New Orleans | LA |
| 1221659.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 1222043.0 | 70.0 | EWR | MSY | New Orleans | LA |
| 1230719.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1231659.0 | 94.0 | EWR | MSY | New Orleans | LA |
| 1231230.0 | 111.0 | EWR | MSY | New Orleans | LA |
| 1232043.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 1241659.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1242043.0 | 56.0 | EWR | MSY | New Orleans | LA |
| 1250719.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 1261654.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 1261230.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1262043.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 1300719.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1301659.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 1302043.0 | 93.0 | EWR | MSY | New Orleans | LA |
| 1310719.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 1311230.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1312043.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 1011815.0 | 125.0 | EWR | MSY | New Orleans | LA |
| 1021815.0 | 33.0 | EWR | MSY | New Orleans | LA |
| 1051815.0 | 172.0 | EWR | MSY | New Orleans | LA |
| 1061815.0 | 151.0 | EWR | MSY | New Orleans | LA |
| 1071815.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1081815.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1091815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1101815.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 1141815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1151815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1161815.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1171815.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1180730.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1191815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1221815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1241815.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 2021025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 2041025.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 2051750.0 | 29.0 | LAS | MSY | New Orleans | LA |
| 2061025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2061750.0 | 48.0 | LAS | MSY | New Orleans | LA |
| 2071750.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 2081055.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2091750.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 2111025.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2121750.0 | 158.0 | LAS | MSY | New Orleans | LA |
| 2131855.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2131215.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 2141855.0 | 22.0 | LAS | MSY | New Orleans | LA |
| 2141215.0 | 18.0 | LAS | MSY | New Orleans | LA |
| 2151635.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2161855.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2161215.0 | 17.0 | LAS | MSY | New Orleans | LA |
| 2171855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2171215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2181855.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2181215.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2191215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2201855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2201215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 2211855.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2211215.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2221635.0 | 133.0 | LAS | MSY | New Orleans | LA |
| 2231215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2241215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2251855.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 2251215.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2261855.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2261215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2271855.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2271215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2281855.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 2281215.0 | 94.0 | LAS | MSY | New Orleans | LA |
| 3010935.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3011635.0 | 39.0 | LAS | MSY | New Orleans | LA |
| 3031855.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 3031215.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 3041215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 3051215.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 3061855.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 3061215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3071855.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3071215.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3080830.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 3091905.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3101905.0 | 49.0 | LAS | MSY | New Orleans | LA |
| 3111905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3110840.0 | 84.0 | LAS | MSY | New Orleans | LA |
| 3121905.0 | 26.0 | LAS | MSY | New Orleans | LA |
| 3131905.0 | 37.0 | LAS | MSY | New Orleans | LA |
| 3130840.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 3141905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3150830.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3151940.0 | 95.0 | LAS | MSY | New Orleans | LA |
| 3171905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3170840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3181905.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 3180840.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 3201905.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3200840.0 | 68.0 | LAS | MSY | New Orleans | LA |
| 3220830.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 3221940.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 3231905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3230840.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3241905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3240840.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3251905.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3261905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3260840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3271905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3280840.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 3291940.0 | 231.0 | LAS | MSY | New Orleans | LA |
| 3301905.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 3300840.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 3311905.0 | 19.0 | LAS | MSY | New Orleans | LA |
| 1011755.0 | 160.0 | LAS | MSY | New Orleans | LA |
| 1021805.0 | 138.0 | LAS | MSY | New Orleans | LA |
| 1020905.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1031805.0 | 154.0 | LAS | MSY | New Orleans | LA |
| 1030905.0 | 179.0 | LAS | MSY | New Orleans | LA |
| 1041655.0 | 113.0 | LAS | MSY | New Orleans | LA |
| 1040900.0 | 56.0 | LAS | MSY | New Orleans | LA |
| 1051805.0 | 53.0 | LAS | MSY | New Orleans | LA |
| 1061755.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 1060905.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 1071025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1071750.0 | 302.0 | LAS | MSY | New Orleans | LA |
| 1081025.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 1081750.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 1091750.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 1101750.0 | 92.0 | LAS | MSY | New Orleans | LA |
| 1111055.0 | 31.0 | LAS | MSY | New Orleans | LA |
| 1121025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1141750.0 | 127.0 | LAS | MSY | New Orleans | LA |
| 1151025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1171025.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 1191025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1201750.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 1211025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1211750.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 1221750.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1231750.0 | 30.0 | LAS | MSY | New Orleans | LA |
| 1241025.0 | 43.0 | LAS | MSY | New Orleans | LA |
| 1251055.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1261750.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 1271750.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 1291025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1301750.0 | 35.0 | LAS | MSY | New Orleans | LA |
| 1311025.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 1311750.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 2041105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 2051105.0 | 6.0 | MCI | MSY | New Orleans | LA |
| 2061105.0 | 40.0 | MCI | MSY | New Orleans | LA |
| 2071105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2081530.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2121105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2140830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2150750.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2170830.0 | 10.0 | MCI | MSY | New Orleans | LA |
| 2180830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2200830.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 2230930.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2240830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2260830.0 | 251.0 | MCI | MSY | New Orleans | LA |
| 3020930.0 | 35.0 | MCI | MSY | New Orleans | LA |
| 3030830.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3040830.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 3050830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 3081610.0 | 93.0 | MCI | MSY | New Orleans | LA |
| 3100950.0 | 8.0 | MCI | MSY | New Orleans | LA |
| 3110950.0 | 36.0 | MCI | MSY | New Orleans | LA |
| 3120950.0 | 68.0 | MCI | MSY | New Orleans | LA |
| 3130950.0 | 13.0 | MCI | MSY | New Orleans | LA |
| 3140950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3151610.0 | 16.0 | MCI | MSY | New Orleans | LA |
| 3180950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3200950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 3210950.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 3221610.0 | 38.0 | MCI | MSY | New Orleans | LA |
| 3250950.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 3260950.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 3270950.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3291610.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3310950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1021635.0 | 96.0 | MCI | MSY | New Orleans | LA |
| 1031635.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1041205.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1051635.0 | 60.0 | MCI | MSY | New Orleans | LA |
| 1071105.0 | 14.0 | MCI | MSY | New Orleans | LA |
| 1081105.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 1091105.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1111530.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1141105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1151105.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 1161105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1181530.0 | 48.0 | MCI | MSY | New Orleans | LA |
| 1211105.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 1221105.0 | 19.0 | MCI | MSY | New Orleans | LA |
| 1241105.0 | 72.0 | MCI | MSY | New Orleans | LA |
| 1251530.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 1301105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1311105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3011530.0 | 72.0 | BNA | MSY | New Orleans | LA |
| 3010850.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 3011245.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3021610.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 3021350.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3021800.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3031610.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3041350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3041800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3050810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3051350.0 | 48.0 | BNA | MSY | New Orleans | LA |
| 3051800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3061610.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 3061350.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3061800.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3071610.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3071350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3071800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3081540.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3080945.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3091820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3091420.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3101420.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3101820.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3101620.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3110820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3111420.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3111820.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3111620.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3120820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3121420.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3121820.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3121620.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 3130820.0 | 126.0 | BNA | MSY | New Orleans | LA |
| 3131420.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3131820.0 | 55.0 | BNA | MSY | New Orleans | LA |
| 3131620.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3141420.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3141820.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3141620.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3150945.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3161620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3161820.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 3170820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3171420.0 | 112.0 | BNA | MSY | New Orleans | LA |
| 3171820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3171620.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3181420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3181820.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3181620.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3191420.0 | 54.0 | BNA | MSY | New Orleans | LA |
| 3191820.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3191620.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3201420.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 3201820.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 3201620.0 | 79.0 | BNA | MSY | New Orleans | LA |
| 3210820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3211420.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3211820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3211620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3231820.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 3241420.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3241820.0 | 44.0 | BNA | MSY | New Orleans | LA |
| 3241620.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3251420.0 | 70.0 | BNA | MSY | New Orleans | LA |
| 3251820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3251620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3261420.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 3261820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3271420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3271820.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3271620.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 3281420.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 3281820.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 3281620.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3291335.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3290945.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 3291540.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3301820.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3301420.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3311420.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 3311820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3311620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 1011210.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1011835.0 | 53.0 | BNA | MSY | New Orleans | LA |
| 1021215.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 1021835.0 | 200.0 | BNA | MSY | New Orleans | LA |
| 1020800.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 1031215.0 | 185.0 | BNA | MSY | New Orleans | LA |
| 1031835.0 | 226.0 | BNA | MSY | New Orleans | LA |
| 1041420.0 | 174.0 | BNA | MSY | New Orleans | LA |
| 1040835.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1051215.0 | 85.0 | BNA | MSY | New Orleans | LA |
| 1051835.0 | 283.0 | BNA | MSY | New Orleans | LA |
| 1050800.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1061215.0 | 150.0 | BNA | MSY | New Orleans | LA |
| 1061835.0 | 133.0 | BNA | MSY | New Orleans | LA |
| 1060800.0 | 102.0 | BNA | MSY | New Orleans | LA |
| 1071240.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1071455.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1070905.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1071735.0 | 131.0 | BNA | MSY | New Orleans | LA |
| 1081240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1081455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1081735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 1091240.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 1091455.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 1091735.0 | 28.0 | BNA | MSY | New Orleans | LA |
| 1101240.0 | 50.0 | BNA | MSY | New Orleans | LA |
| 1101455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 1100905.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1101735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1111805.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1121235.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1121455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1121735.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1131240.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1131455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1131735.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 1141240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1141735.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1151455.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 1151735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1161240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161455.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161735.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 1171240.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 1171455.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1170850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1171735.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 1181305.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1181805.0 | 42.0 | BNA | MSY | New Orleans | LA |
| 1180950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191235.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 1191455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191735.0 | 64.0 | BNA | MSY | New Orleans | LA |
| 1201240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1201455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1200850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1211240.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 1210850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1211735.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1221240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1221735.0 | 118.0 | BNA | MSY | New Orleans | LA |
| 1231240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1231455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1230850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1231735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1241240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1241455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1240850.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1251305.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 1251805.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1261455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1261735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1271240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1271735.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1291240.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1291735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1301240.0 | 38.0 | BNA | MSY | New Orleans | LA |
| 1301455.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 1301735.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1311240.0 | 31.0 | BNA | MSY | New Orleans | LA |
| 1311455.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 1311735.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2010950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 2021455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2021235.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2021650.0 | 77.0 | BNA | MSY | New Orleans | LA |
| 2031240.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 2031455.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2041240.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 2041455.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2041735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2051240.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 2051455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2050850.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 2051735.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2061240.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 2060850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2061735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 2071240.0 | 88.0 | BNA | MSY | New Orleans | LA |
| 2071455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2070850.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2071735.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 2091235.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2091455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2091735.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2101240.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2101455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2100850.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2111240.0 | 145.0 | BNA | MSY | New Orleans | LA |
| 2110850.0 | 96.0 | BNA | MSY | New Orleans | LA |
| 2111735.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2121240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2121455.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 2120850.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2121735.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 2131350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2131610.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2131800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2141610.0 | 37.0 | BNA | MSY | New Orleans | LA |
| 2140810.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 2141350.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2141800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 2151530.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 2150850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2161800.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 2170810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2171350.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2180810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2181350.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 2181800.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2191350.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2191800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 2201610.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2200810.0 | 16.0 | BNA | MSY | New Orleans | LA |
| 2201350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2201800.0 | 162.0 | BNA | MSY | New Orleans | LA |
| 2211610.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2210810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2211350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2211800.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2221530.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2231350.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2231800.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2241350.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 2250810.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2251800.0 | 103.0 | BNA | MSY | New Orleans | LA |
| 2261610.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2260810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2261350.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2261800.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2271800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2281610.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2281350.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2281800.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3011117.0 | 36.0 | CLT | MSY | New Orleans | LA |
| 3011635.0 | 77.0 | CLT | MSY | New Orleans | LA |
| 3021117.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 3031825.0 | 9.0 | CLT | MSY | New Orleans | LA |
| 3061825.0 | 32.0 | CLT | MSY | New Orleans | LA |
| 3062010.0 | 33.0 | CLT | MSY | New Orleans | LA |
| 3070750.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3071435.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 3081115.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3091115.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3090909.0 | 118.0 | CLT | MSY | New Orleans | LA |
| 3102010.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 3110750.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 3121115.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 3122010.0 | 31.0 | CLT | MSY | New Orleans | LA |
| 3121435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3141825.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3141115.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3140915.0 | 17.0 | CLT | MSY | New Orleans | LA |
| 3161840.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 3162010.0 | 34.0 | CLT | MSY | New Orleans | LA |
| 3161435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3171825.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 3171115.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3172010.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3171435.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3170915.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3180750.0 | 46.0 | CLT | MSY | New Orleans | LA |
| 3182010.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3180915.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3190915.0 | 65.0 | CLT | MSY | New Orleans | LA |
| 3202010.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 3201435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3210750.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3230909.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 3241115.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 3240915.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 3251435.0 | 56.0 | CLT | MSY | New Orleans | LA |
| 3250915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3261115.0 | 9.0 | CLT | MSY | New Orleans | LA |
| 3261435.0 | 96.0 | CLT | MSY | New Orleans | LA |
| 3260915.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3271115.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3270915.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 3281115.0 | 37.0 | CLT | MSY | New Orleans | LA |
| 3282010.0 | 46.0 | CLT | MSY | New Orleans | LA |
| 3291825.0 | 150.0 | CLT | MSY | New Orleans | LA |
| 3291115.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3292010.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 3291635.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3290915.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3311115.0 | 18.0 | CLT | MSY | New Orleans | LA |
| 3311435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1011815.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 1011435.0 | 40.0 | CLT | MSY | New Orleans | LA |
| 1021815.0 | 22.0 | CLT | MSY | New Orleans | LA |
| 1022015.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1021435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1042020.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 1041435.0 | 15.0 | CLT | MSY | New Orleans | LA |
| 1051815.0 | 45.0 | CLT | MSY | New Orleans | LA |
| 1052015.0 | 19.0 | CLT | MSY | New Orleans | LA |
| 1051435.0 | 60.0 | CLT | MSY | New Orleans | LA |
| 1060750.0 | 7.0 | CLT | MSY | New Orleans | LA |
| 1061120.0 | 15.0 | CLT | MSY | New Orleans | LA |
| 1071825.0 | 58.0 | CLT | MSY | New Orleans | LA |
| 1071435.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 1081435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1080915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1091117.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1100750.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 1101825.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 1101117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1101635.0 | 55.0 | CLT | MSY | New Orleans | LA |
| 1101435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1110750.0 | 115.0 | CLT | MSY | New Orleans | LA |
| 1111117.0 | 49.0 | CLT | MSY | New Orleans | LA |
| 1111635.0 | 145.0 | CLT | MSY | New Orleans | LA |
| 1112010.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1111435.0 | 126.0 | CLT | MSY | New Orleans | LA |
| 1110915.0 | 72.0 | CLT | MSY | New Orleans | LA |
| 1121117.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 1121435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1131435.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 1151117.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 1151435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1171117.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 1181117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 1180915.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 1191117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1200750.0 | 51.0 | CLT | MSY | New Orleans | LA |
| 1211825.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1211435.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 1221117.0 | 10.0 | CLT | MSY | New Orleans | LA |
| 1221435.0 | 25.0 | CLT | MSY | New Orleans | LA |
| 1230750.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 1231117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1231635.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 1230915.0 | 131.0 | CLT | MSY | New Orleans | LA |
| 1241117.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 1252010.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1250915.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1271825.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1271117.0 | 52.0 | CLT | MSY | New Orleans | LA |
| 1290750.0 | 26.0 | CLT | MSY | New Orleans | LA |
| 1291117.0 | 19.0 | CLT | MSY | New Orleans | LA |
| 1291635.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 1291435.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 1290915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1311825.0 | 50.0 | CLT | MSY | New Orleans | LA |
| 1310915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 2011117.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 2011635.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 2010900.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2031117.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 2031435.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 2041117.0 | 22.0 | CLT | MSY | New Orleans | LA |
| 2051117.0 | 36.0 | CLT | MSY | New Orleans | LA |
| 2051435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 2050915.0 | 7.0 | CLT | MSY | New Orleans | LA |
| 2060750.0 | 34.0 | CLT | MSY | New Orleans | LA |
| 2061117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 2061635.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 2071825.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 2090915.0 | 79.0 | CLT | MSY | New Orleans | LA |
| 2101435.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 2121635.0 | 42.0 | CLT | MSY | New Orleans | LA |
| 2121435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 2140750.0 | 151.0 | CLT | MSY | New Orleans | LA |
| 2141825.0 | 33.0 | CLT | MSY | New Orleans | LA |
| 2142010.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 2141435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 2140915.0 | 174.0 | CLT | MSY | New Orleans | LA |
| 2150915.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 2161117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 2161435.0 | 12.0 | CLT | MSY | New Orleans | LA |
| 2171117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2171435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2170915.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 2200915.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 2211117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2211435.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 2210915.0 | 52.0 | CLT | MSY | New Orleans | LA |
| 2232010.0 | 74.0 | CLT | MSY | New Orleans | LA |
| 2251435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 2261825.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 2272010.0 | 10.0 | CLT | MSY | New Orleans | LA |
| 2271435.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 2280750.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2281117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3011715.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3011830.0 | 124.0 | DAL | MSY | New Orleans | LA |
| 3021710.0 | 174.0 | DAL | MSY | New Orleans | LA |
| 3021335.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3022025.0 | 84.0 | DAL | MSY | New Orleans | LA |
| 3021855.0 | 55.0 | DAL | MSY | New Orleans | LA |
| 3030605.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3031335.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3032025.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3030920.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3031710.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3031100.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3031855.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3041335.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3042025.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3052025.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3051710.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 3051855.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3061335.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 3062025.0 | 52.0 | DAL | MSY | New Orleans | LA |
| 3060920.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3061710.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3061100.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3070805.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3071335.0 | 60.0 | DAL | MSY | New Orleans | LA |
| 3072025.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 3071710.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 3071100.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3071855.0 | 66.0 | DAL | MSY | New Orleans | LA |
| 3081440.0 | 107.0 | DAL | MSY | New Orleans | LA |
| 3081745.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3091900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3092055.0 | 35.0 | DAL | MSY | New Orleans | LA |
| 3091810.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3091455.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3101900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3102055.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 3101810.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3111900.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3112055.0 | 103.0 | DAL | MSY | New Orleans | LA |
| 3111205.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3110825.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3111810.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3111455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3121900.0 | 50.0 | DAL | MSY | New Orleans | LA |
| 3122055.0 | 57.0 | DAL | MSY | New Orleans | LA |
| 3121205.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3120825.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3121810.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3131900.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 3130930.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3132055.0 | 77.0 | DAL | MSY | New Orleans | LA |
| 3130600.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3130825.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3131810.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3141900.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 3142055.0 | 25.0 | DAL | MSY | New Orleans | LA |
| 3140600.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3141205.0 | 54.0 | DAL | MSY | New Orleans | LA |
| 3141810.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3141455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3150830.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3151440.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3161900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3162055.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3161810.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3160930.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3171900.0 | 83.0 | DAL | MSY | New Orleans | LA |
| 3170930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3172055.0 | 81.0 | DAL | MSY | New Orleans | LA |
| 3170825.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3171810.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3181900.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 3182055.0 | 148.0 | DAL | MSY | New Orleans | LA |
| 3180600.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3181205.0 | 32.0 | DAL | MSY | New Orleans | LA |
| 3180825.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3181810.0 | 92.0 | DAL | MSY | New Orleans | LA |
| 3181455.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3191900.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3192055.0 | 71.0 | DAL | MSY | New Orleans | LA |
| 3191205.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3191810.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3191455.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3201900.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 3202055.0 | 34.0 | DAL | MSY | New Orleans | LA |
| 3200600.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3201205.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3200825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3201810.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 3211900.0 | 52.0 | DAL | MSY | New Orleans | LA |
| 3212055.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3210600.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3211205.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3210825.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3211810.0 | 14.0 | DAL | MSY | New Orleans | LA |
| 3211455.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3221750.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3221440.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3231900.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3231810.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3231455.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3231210.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3231045.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3230930.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3241900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3242055.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3241205.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3240825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3241810.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3241455.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3251900.0 | 58.0 | DAL | MSY | New Orleans | LA |
| 3252055.0 | 51.0 | DAL | MSY | New Orleans | LA |
| 3251205.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3250825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3251810.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3251455.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3261900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3262055.0 | 69.0 | DAL | MSY | New Orleans | LA |
| 3261205.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3260825.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3261810.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3261455.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3271900.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3272055.0 | 78.0 | DAL | MSY | New Orleans | LA |
| 3271205.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3270825.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3271810.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 3271455.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3281900.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 3280930.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3282055.0 | 65.0 | DAL | MSY | New Orleans | LA |
| 3281205.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3280825.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3281810.0 | 49.0 | DAL | MSY | New Orleans | LA |
| 3281455.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3290830.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3291440.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3301900.0 | 126.0 | DAL | MSY | New Orleans | LA |
| 3301810.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3301455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3301045.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3300930.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3311900.0 | 47.0 | DAL | MSY | New Orleans | LA |
| 3310930.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3312055.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3311205.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3310825.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 3311810.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 3311455.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1012115.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1011235.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1011650.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 1011525.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 1011120.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1021120.0 | 40.0 | DAL | MSY | New Orleans | LA |
| 1021650.0 | 37.0 | DAL | MSY | New Orleans | LA |
| 1020925.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1022110.0 | 115.0 | DAL | MSY | New Orleans | LA |
| 1021235.0 | 66.0 | DAL | MSY | New Orleans | LA |
| 1020605.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 1021525.0 | 91.0 | DAL | MSY | New Orleans | LA |
| 1021910.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1031120.0 | 121.0 | DAL | MSY | New Orleans | LA |
| 1031650.0 | 154.0 | DAL | MSY | New Orleans | LA |
| 1030925.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 1032110.0 | 120.0 | DAL | MSY | New Orleans | LA |
| 1031235.0 | 43.0 | DAL | MSY | New Orleans | LA |
| 1031525.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 1031910.0 | 194.0 | DAL | MSY | New Orleans | LA |
| 1040710.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1041730.0 | 108.0 | DAL | MSY | New Orleans | LA |
| 1041420.0 | 49.0 | DAL | MSY | New Orleans | LA |
| 1040930.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 1041540.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1052110.0 | 110.0 | DAL | MSY | New Orleans | LA |
| 1051235.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1051525.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1051150.0 | 32.0 | DAL | MSY | New Orleans | LA |
| 1051910.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1050925.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 1051650.0 | 101.0 | DAL | MSY | New Orleans | LA |
| 1061120.0 | 48.0 | DAL | MSY | New Orleans | LA |
| 1061650.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1062110.0 | 103.0 | DAL | MSY | New Orleans | LA |
| 1061235.0 | 71.0 | DAL | MSY | New Orleans | LA |
| 1061525.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 1072015.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 1071535.0 | 53.0 | DAL | MSY | New Orleans | LA |
| 1070630.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1071930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1070925.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1071025.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 1071230.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1071635.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1082015.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 1081535.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 1081930.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 1080925.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1081025.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1081230.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1081635.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1092015.0 | 89.0 | DAL | MSY | New Orleans | LA |
| 1091535.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 1090925.0 | 39.0 | DAL | MSY | New Orleans | LA |
| 1091025.0 | 94.0 | DAL | MSY | New Orleans | LA |
| 1091230.0 | 73.0 | DAL | MSY | New Orleans | LA |
| 1091635.0 | 35.0 | DAL | MSY | New Orleans | LA |
| 1102015.0 | 100.0 | DAL | MSY | New Orleans | LA |
| 1101535.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 1101930.0 | 118.0 | DAL | MSY | New Orleans | LA |
| 1100925.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1101025.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 1101230.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 1101635.0 | 57.0 | DAL | MSY | New Orleans | LA |
| 1111750.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 1110645.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1111450.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1111625.0 | 50.0 | DAL | MSY | New Orleans | LA |
| 1122015.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1121535.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1121620.0 | 33.0 | DAL | MSY | New Orleans | LA |
| 1121930.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1120925.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1121230.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1131535.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1130630.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1130925.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 1131025.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 1142015.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1140630.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1140925.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1141025.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 1141230.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1141635.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1151930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1150925.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1151025.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 1162015.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1161535.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 1161930.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1160925.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1161025.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1161230.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1161635.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 1172015.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1171535.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1171025.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1171230.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1171635.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 1180645.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 1181450.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1181625.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 1191535.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1191620.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1191930.0 | 219.0 | DAL | MSY | New Orleans | LA |
| 1190925.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1200925.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1201025.0 | 33.0 | DAL | MSY | New Orleans | LA |
| 1201230.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1201635.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1211535.0 | 95.0 | DAL | MSY | New Orleans | LA |
// States with the longest cumulative delays (with individual delays > 100 minutes) (origin: Seattle)
display(tripGraph.edges.filter($"src" === "SEA" && $"delay" > 100))
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 3201938.0 | 108.0 | SEA | BUR | Burbank | CA |
| 3201655.0 | 107.0 | SEA | SNA | Orange County | CA |
| 1011950.0 | 123.0 | SEA | OAK | Oakland | CA |
| 1021950.0 | 194.0 | SEA | OAK | Oakland | CA |
| 1021615.0 | 317.0 | SEA | OAK | Oakland | CA |
| 1021755.0 | 385.0 | SEA | OAK | Oakland | CA |
| 1031950.0 | 283.0 | SEA | OAK | Oakland | CA |
| 1031615.0 | 364.0 | SEA | OAK | Oakland | CA |
| 1031325.0 | 130.0 | SEA | OAK | Oakland | CA |
| 1061755.0 | 107.0 | SEA | OAK | Oakland | CA |
| 1081330.0 | 118.0 | SEA | OAK | Oakland | CA |
| 2282055.0 | 150.0 | SEA | OAK | Oakland | CA |
| 3061600.0 | 130.0 | SEA | OAK | Oakland | CA |
| 3170815.0 | 199.0 | SEA | DCA | Washington DC | null |
| 2151845.0 | 128.0 | SEA | KTN | Ketchikan | AK |
| 2281845.0 | 104.0 | SEA | KTN | Ketchikan | AK |
| 3130720.0 | 117.0 | SEA | KTN | Ketchikan | AK |
| 1011411.0 | 177.0 | SEA | IAH | Houston | TX |
| 1022347.0 | 158.0 | SEA | IAH | Houston | TX |
| 1021411.0 | 170.0 | SEA | IAH | Houston | TX |
| 1031201.0 | 116.0 | SEA | IAH | Houston | TX |
| 1031900.0 | 178.0 | SEA | IAH | Houston | TX |
| 1042347.0 | 114.0 | SEA | IAH | Houston | TX |
| 1062347.0 | 136.0 | SEA | IAH | Houston | TX |
| 1101203.0 | 173.0 | SEA | IAH | Houston | TX |
| 1172300.0 | 125.0 | SEA | IAH | Houston | TX |
| 1250605.0 | 227.0 | SEA | IAH | Houston | TX |
| 1291203.0 | 106.0 | SEA | IAH | Houston | TX |
| 2141157.0 | 127.0 | SEA | IAH | Houston | TX |
| 2150740.0 | 466.0 | SEA | IAH | Houston | TX |
| 2180750.0 | 105.0 | SEA | IAH | Houston | TX |
| 3010740.0 | 103.0 | SEA | IAH | Houston | TX |
| 3021152.0 | 124.0 | SEA | IAH | Houston | TX |
| 3121152.0 | 340.0 | SEA | IAH | Houston | TX |
| 3140600.0 | 139.0 | SEA | IAH | Houston | TX |
| 3171152.0 | 121.0 | SEA | IAH | Houston | TX |
| 3280600.0 | 103.0 | SEA | IAH | Houston | TX |
| 1151746.0 | 229.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1271746.0 | 111.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1030840.0 | 294.0 | SEA | HNL | Honolulu, Oahu | HI |
| 2091035.0 | 132.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3141035.0 | 295.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3141930.0 | 128.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3311930.0 | 104.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3030840.0 | 185.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3240845.0 | 114.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1041740.0 | 256.0 | SEA | SJC | San Jose | CA |
| 1061815.0 | 200.0 | SEA | SJC | San Jose | CA |
| 3031600.0 | 161.0 | SEA | SJC | San Jose | CA |
| 1031100.0 | 145.0 | SEA | LGB | Long Beach | CA |
| 1041737.0 | 320.0 | SEA | LGB | Long Beach | CA |
| 1081728.0 | 122.0 | SEA | LGB | Long Beach | CA |
| 1221140.0 | 189.0 | SEA | LGB | Long Beach | CA |
| 2121140.0 | 365.0 | SEA | LGB | Long Beach | CA |
| 3070710.0 | 115.0 | SEA | LGB | Long Beach | CA |
| 3180935.0 | 135.0 | SEA | LGB | Long Beach | CA |
| 2141245.0 | 176.0 | SEA | RNO | Reno | NV |
| 1052147.0 | 110.0 | SEA | BOS | Boston | MA |
| 1081420.0 | 109.0 | SEA | BOS | Boston | MA |
| 1112313.0 | 110.0 | SEA | BOS | Boston | MA |
| 1232313.0 | 110.0 | SEA | BOS | Boston | MA |
| 2140945.0 | 105.0 | SEA | BOS | Boston | MA |
| 2032313.0 | 114.0 | SEA | BOS | Boston | MA |
| 2121420.0 | 108.0 | SEA | BOS | Boston | MA |
| 2141415.0 | 152.0 | SEA | BOS | Boston | MA |
| 3282307.0 | 119.0 | SEA | BOS | Boston | MA |
| 1110905.0 | 130.0 | SEA | EWR | Newark | NJ |
| 1022227.0 | 236.0 | SEA | EWR | Newark | NJ |
| 1180703.0 | 138.0 | SEA | EWR | Newark | NJ |
| 2030905.0 | 212.0 | SEA | EWR | Newark | NJ |
| 3141530.0 | 131.0 | SEA | EWR | Newark | NJ |
| 3171530.0 | 181.0 | SEA | EWR | Newark | NJ |
| 3202200.0 | 168.0 | SEA | EWR | Newark | NJ |
| 1032115.0 | 196.0 | SEA | LAS | Las Vegas | NV |
| 1201840.0 | 113.0 | SEA | LAS | Las Vegas | NV |
| 1260840.0 | 165.0 | SEA | LAS | Las Vegas | NV |
| 1261840.0 | 121.0 | SEA | LAS | Las Vegas | NV |
| 1031905.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 1041550.0 | 156.0 | SEA | LAS | Las Vegas | NV |
| 1061820.0 | 213.0 | SEA | LAS | Las Vegas | NV |
| 1061515.0 | 116.0 | SEA | LAS | Las Vegas | NV |
| 1080805.0 | 235.0 | SEA | LAS | Las Vegas | NV |
| 1170800.0 | 247.0 | SEA | LAS | Las Vegas | NV |
| 2191235.0 | 210.0 | SEA | LAS | Las Vegas | NV |
| 2281430.0 | 121.0 | SEA | LAS | Las Vegas | NV |
| 2281235.0 | 187.0 | SEA | LAS | Las Vegas | NV |
| 2170830.0 | 610.0 | SEA | LAS | Las Vegas | NV |
| 2051205.0 | 110.0 | SEA | LAS | Las Vegas | NV |
| 2111835.0 | 133.0 | SEA | LAS | Las Vegas | NV |
| 2132005.0 | 135.0 | SEA | LAS | Las Vegas | NV |
| 2272005.0 | 102.0 | SEA | LAS | Las Vegas | NV |
| 3031430.0 | 108.0 | SEA | LAS | Las Vegas | NV |
| 3141410.0 | 102.0 | SEA | LAS | Las Vegas | NV |
| 3151205.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 3281245.0 | 140.0 | SEA | LAS | Las Vegas | NV |
| 3181520.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 3292000.0 | 107.0 | SEA | LAS | Las Vegas | NV |
| 1051955.0 | 160.0 | SEA | FAI | Fairbanks | AK |
| 1040955.0 | 126.0 | SEA | DEN | Denver | CO |
| 1040650.0 | 103.0 | SEA | DEN | Denver | CO |
| 1160650.0 | 120.0 | SEA | DEN | Denver | CO |
| 1011940.0 | 167.0 | SEA | DEN | Denver | CO |
| 1041039.0 | 113.0 | SEA | DEN | Denver | CO |
| 1041437.0 | 256.0 | SEA | DEN | Denver | CO |
| 1041937.0 | 191.0 | SEA | DEN | Denver | CO |
| 1040605.0 | 138.0 | SEA | DEN | Denver | CO |
| 1061937.0 | 111.0 | SEA | DEN | Denver | CO |
| 1121937.0 | 118.0 | SEA | DEN | Denver | CO |
| 1171937.0 | 104.0 | SEA | DEN | Denver | CO |
| 1021523.0 | 118.0 | SEA | DEN | Denver | CO |
| 1041521.0 | 177.0 | SEA | DEN | Denver | CO |
| 1061055.0 | 154.0 | SEA | DEN | Denver | CO |
| 1281515.0 | 137.0 | SEA | DEN | Denver | CO |
| 1011450.0 | 102.0 | SEA | DEN | Denver | CO |
| 1021205.0 | 425.0 | SEA | DEN | Denver | CO |
| 1031450.0 | 211.0 | SEA | DEN | Denver | CO |
| 2210955.0 | 132.0 | SEA | DEN | Denver | CO |
| 2111537.0 | 156.0 | SEA | DEN | Denver | CO |
| 2110700.0 | 625.0 | SEA | DEN | Denver | CO |
| 2191039.0 | 148.0 | SEA | DEN | Denver | CO |
| 2211039.0 | 105.0 | SEA | DEN | Denver | CO |
| 2031055.0 | 174.0 | SEA | DEN | Denver | CO |
| 2051055.0 | 112.0 | SEA | DEN | Denver | CO |
| 2211110.0 | 106.0 | SEA | DEN | Denver | CO |
| 3131405.0 | 154.0 | SEA | DEN | Denver | CO |
| 3181930.0 | 115.0 | SEA | DEN | Denver | CO |
| 3191749.0 | 103.0 | SEA | DEN | Denver | CO |
| 3011405.0 | 109.0 | SEA | DEN | Denver | CO |
| 3121519.0 | 231.0 | SEA | DEN | Denver | CO |
| 3061445.0 | 148.0 | SEA | DEN | Denver | CO |
| 3181440.0 | 132.0 | SEA | DEN | Denver | CO |
| 1011306.0 | 206.0 | SEA | IAD | Washington DC | null |
| 1050806.0 | 105.0 | SEA | IAD | Washington DC | null |
| 1052226.0 | 111.0 | SEA | IAD | Washington DC | null |
| 1291256.0 | 108.0 | SEA | IAD | Washington DC | null |
| 2031256.0 | 185.0 | SEA | IAD | Washington DC | null |
| 2041256.0 | 129.0 | SEA | IAD | Washington DC | null |
| 2101256.0 | 144.0 | SEA | IAD | Washington DC | null |
| 2191316.0 | 122.0 | SEA | IAD | Washington DC | null |
| 3080800.0 | 273.0 | SEA | IAD | Washington DC | null |
| 3162225.0 | 174.0 | SEA | IAD | Washington DC | null |
| 3201316.0 | 111.0 | SEA | IAD | Washington DC | null |
| 3261311.0 | 104.0 | SEA | IAD | Washington DC | null |
| 1111300.0 | 133.0 | SEA | PSP | Palm Springs | CA |
| 1110910.0 | 171.0 | SEA | PSP | Palm Springs | CA |
| 3170915.0 | 114.0 | SEA | PSP | Palm Springs | CA |
| 3310645.0 | 179.0 | SEA | PSP | Palm Springs | CA |
| 1161050.0 | 132.0 | SEA | SBA | Santa Barbara | CA |
| 2091050.0 | 310.0 | SEA | SBA | Santa Barbara | CA |
| 3191045.0 | 109.0 | SEA | SBA | Santa Barbara | CA |
| 3241045.0 | 140.0 | SEA | SBA | Santa Barbara | CA |
| 2122225.0 | 228.0 | SEA | CLT | Charlotte | NC |
| 2142225.0 | 104.0 | SEA | CLT | Charlotte | NC |
| 2152225.0 | 113.0 | SEA | CLT | Charlotte | NC |
| 3081110.0 | 110.0 | SEA | CLT | Charlotte | NC |
| 1290940.0 | 316.0 | SEA | ABQ | Albuquerque | NM |
| 1171153.0 | 119.0 | SEA | PDX | Portland | OR |
| 1171439.0 | 112.0 | SEA | PDX | Portland | OR |
| 2091041.0 | 309.0 | SEA | PDX | Portland | OR |
| 2090845.0 | 133.0 | SEA | PDX | Portland | OR |
| 3021455.0 | 226.0 | SEA | PDX | Portland | OR |
| 3021914.0 | 165.0 | SEA | PDX | Portland | OR |
| 3090926.0 | 112.0 | SEA | PDX | Portland | OR |
| 3251041.0 | 274.0 | SEA | PDX | Portland | OR |
| 3261728.0 | 109.0 | SEA | PDX | Portland | OR |
| 3301728.0 | 108.0 | SEA | PDX | Portland | OR |
| 1112205.0 | 101.0 | SEA | MIA | Miami | FL |
| 1262205.0 | 130.0 | SEA | MIA | Miami | FL |
| 2142215.0 | 229.0 | SEA | MIA | Miami | FL |
| 3292220.0 | 117.0 | SEA | MIA | Miami | FL |
| 1012140.0 | 136.0 | SEA | SMF | Sacramento | CA |
| 1010715.0 | 164.0 | SEA | SMF | Sacramento | CA |
| 1021930.0 | 109.0 | SEA | SMF | Sacramento | CA |
| 1031630.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 1041440.0 | 137.0 | SEA | SMF | Sacramento | CA |
| 1041710.0 | 102.0 | SEA | SMF | Sacramento | CA |
| 2211915.0 | 103.0 | SEA | SMF | Sacramento | CA |
| 3262140.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 3271855.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 3191020.0 | 139.0 | SEA | SMF | Sacramento | CA |
| 1291410.0 | 136.0 | SEA | PHX | Phoenix | AZ |
| 1040830.0 | 294.0 | SEA | PHX | Phoenix | AZ |
| 1300830.0 | 371.0 | SEA | PHX | Phoenix | AZ |
| 1031510.0 | 119.0 | SEA | PHX | Phoenix | AZ |
| 1061510.0 | 180.0 | SEA | PHX | Phoenix | AZ |
| 1290720.0 | 147.0 | SEA | PHX | Phoenix | AZ |
| 2082020.0 | 130.0 | SEA | PHX | Phoenix | AZ |
| 2281855.0 | 186.0 | SEA | PHX | Phoenix | AZ |
| 2110520.0 | 463.0 | SEA | PHX | Phoenix | AZ |
| 2111132.0 | 107.0 | SEA | PHX | Phoenix | AZ |
| 2160830.0 | 110.0 | SEA | PHX | Phoenix | AZ |
| 2241132.0 | 107.0 | SEA | PHX | Phoenix | AZ |
| 2251615.0 | 103.0 | SEA | PHX | Phoenix | AZ |
| 3041455.0 | 108.0 | SEA | PHX | Phoenix | AZ |
| 3051745.0 | 169.0 | SEA | PHX | Phoenix | AZ |
| 3091455.0 | 126.0 | SEA | PHX | Phoenix | AZ |
| 3251132.0 | 123.0 | SEA | PHX | Phoenix | AZ |
| 3221530.0 | 121.0 | SEA | PHX | Phoenix | AZ |
| 1060830.0 | 116.0 | SEA | DFW | Dallas | TX |
| 1180830.0 | 132.0 | SEA | DFW | Dallas | TX |
| 1281350.0 | 115.0 | SEA | DFW | Dallas | TX |
| 1291545.0 | 247.0 | SEA | DFW | Dallas | TX |
| 2031545.0 | 230.0 | SEA | DFW | Dallas | TX |
| 2040830.0 | 135.0 | SEA | DFW | Dallas | TX |
| 2061115.0 | 110.0 | SEA | DFW | Dallas | TX |
| 2061545.0 | 125.0 | SEA | DFW | Dallas | TX |
| 2061350.0 | 126.0 | SEA | DFW | Dallas | TX |
| 2080830.0 | 356.0 | SEA | DFW | Dallas | TX |
| 2090830.0 | 128.0 | SEA | DFW | Dallas | TX |
| 3012320.0 | 123.0 | SEA | DFW | Dallas | TX |
| 3152320.0 | 143.0 | SEA | DFW | Dallas | TX |
| 3151400.0 | 146.0 | SEA | DFW | Dallas | TX |
| 3301400.0 | 277.0 | SEA | DFW | Dallas | TX |
| 1171220.0 | 110.0 | SEA | SFO | San Francisco | CA |
| 1291220.0 | 103.0 | SEA | SFO | San Francisco | CA |
| 1031125.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 1120924.0 | 268.0 | SEA | SFO | San Francisco | CA |
| 1161058.0 | 131.0 | SEA | SFO | San Francisco | CA |
| 1171058.0 | 138.0 | SEA | SFO | San Francisco | CA |
| 1010955.0 | 104.0 | SEA | SFO | San Francisco | CA |
| 1021914.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 1041830.0 | 131.0 | SEA | SFO | San Francisco | CA |
| 1051214.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 1061214.0 | 384.0 | SEA | SFO | San Francisco | CA |
| 1111310.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 1241310.0 | 133.0 | SEA | SFO | San Francisco | CA |
| 1281310.0 | 250.0 | SEA | SFO | San Francisco | CA |
| 1051855.0 | 166.0 | SEA | SFO | San Francisco | CA |
| 2060955.0 | 148.0 | SEA | SFO | San Francisco | CA |
| 2072125.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2071845.0 | 203.0 | SEA | SFO | San Francisco | CA |
| 2071220.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2071100.0 | 113.0 | SEA | SFO | San Francisco | CA |
| 2071405.0 | 144.0 | SEA | SFO | San Francisco | CA |
| 2080955.0 | 121.0 | SEA | SFO | San Francisco | CA |
| 2091835.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 2091405.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 2101220.0 | 134.0 | SEA | SFO | San Francisco | CA |
| 2100955.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 2101100.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2101405.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2130955.0 | 108.0 | SEA | SFO | San Francisco | CA |
| 2131100.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2141100.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 2281100.0 | 130.0 | SEA | SFO | San Francisco | CA |
| 2231058.0 | 136.0 | SEA | SFO | San Francisco | CA |
| 2021310.0 | 115.0 | SEA | SFO | San Francisco | CA |
| 2091820.0 | 149.0 | SEA | SFO | San Francisco | CA |
| 2181526.0 | 106.0 | SEA | SFO | San Francisco | CA |
| 2271905.0 | 154.0 | SEA | SFO | San Francisco | CA |
| 2021450.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2060950.0 | 150.0 | SEA | SFO | San Francisco | CA |
| 2061450.0 | 140.0 | SEA | SFO | San Francisco | CA |
| 2061855.0 | 119.0 | SEA | SFO | San Francisco | CA |
| 2071855.0 | 146.0 | SEA | SFO | San Francisco | CA |
| 2081330.0 | 164.0 | SEA | SFO | San Francisco | CA |
| 2091450.0 | 106.0 | SEA | SFO | San Francisco | CA |
| 2091855.0 | 182.0 | SEA | SFO | San Francisco | CA |
| 2261450.0 | 259.0 | SEA | SFO | San Francisco | CA |
| 2261855.0 | 224.0 | SEA | SFO | San Francisco | CA |
| 2270950.0 | 174.0 | SEA | SFO | San Francisco | CA |
| 2271450.0 | 240.0 | SEA | SFO | San Francisco | CA |
| 2280950.0 | 207.0 | SEA | SFO | San Francisco | CA |
| 2281450.0 | 331.0 | SEA | SFO | San Francisco | CA |
| 2281855.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 3142043.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 3180950.0 | 155.0 | SEA | SFO | San Francisco | CA |
| 3260950.0 | 117.0 | SEA | SFO | San Francisco | CA |
| 3281430.0 | 112.0 | SEA | SFO | San Francisco | CA |
| 3291050.0 | 138.0 | SEA | SFO | San Francisco | CA |
| 3062055.0 | 113.0 | SEA | SFO | San Francisco | CA |
| 3091028.0 | 143.0 | SEA | SFO | San Francisco | CA |
| 3112055.0 | 116.0 | SEA | SFO | San Francisco | CA |
| 3261043.0 | 121.0 | SEA | SFO | San Francisco | CA |
| 3311043.0 | 257.0 | SEA | SFO | San Francisco | CA |
| 3171237.0 | 182.0 | SEA | SFO | San Francisco | CA |
| 3251933.0 | 197.0 | SEA | SFO | San Francisco | CA |
| 3011330.0 | 115.0 | SEA | SFO | San Francisco | CA |
| 3311045.0 | 189.0 | SEA | SFO | San Francisco | CA |
| 3311440.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 1031152.0 | 397.0 | SEA | ATL | Atlanta | GA |
| 1050830.0 | 106.0 | SEA | ATL | Atlanta | GA |
| 1051152.0 | 107.0 | SEA | ATL | Atlanta | GA |
| 1121159.0 | 201.0 | SEA | ATL | Atlanta | GA |
| 1250630.0 | 206.0 | SEA | ATL | Atlanta | GA |
| 1301159.0 | 117.0 | SEA | ATL | Atlanta | GA |
| 1301322.0 | 179.0 | SEA | ATL | Atlanta | GA |
| 2021159.0 | 145.0 | SEA | ATL | Atlanta | GA |
| 2171320.0 | 133.0 | SEA | ATL | Atlanta | GA |
| 3171320.0 | 109.0 | SEA | ATL | Atlanta | GA |
| 1092015.0 | 119.0 | SEA | FAT | Fresno | CA |
| 2012015.0 | 189.0 | SEA | FAT | Fresno | CA |
| 2091150.0 | 232.0 | SEA | FAT | Fresno | CA |
| 1021425.0 | 298.0 | SEA | ORD | Chicago | IL |
| 1030600.0 | 103.0 | SEA | ORD | Chicago | IL |
| 1081205.0 | 135.0 | SEA | ORD | Chicago | IL |
| 1161205.0 | 582.0 | SEA | ORD | Chicago | IL |
| 1241205.0 | 174.0 | SEA | ORD | Chicago | IL |
| 1300815.0 | 209.0 | SEA | ORD | Chicago | IL |
| 1021235.0 | 113.0 | SEA | ORD | Chicago | IL |
| 1020830.0 | 151.0 | SEA | ORD | Chicago | IL |
| 1051235.0 | 240.0 | SEA | ORD | Chicago | IL |
| 1050830.0 | 163.0 | SEA | ORD | Chicago | IL |
| 1241235.0 | 171.0 | SEA | ORD | Chicago | IL |
| 1301235.0 | 111.0 | SEA | ORD | Chicago | IL |
| 1300830.0 | 141.0 | SEA | ORD | Chicago | IL |
| 1012359.0 | 227.0 | SEA | ORD | Chicago | IL |
| 1040859.0 | 302.0 | SEA | ORD | Chicago | IL |
| 1241110.0 | 223.0 | SEA | ORD | Chicago | IL |
| 1311411.0 | 142.0 | SEA | ORD | Chicago | IL |
| 2051235.0 | 115.0 | SEA | ORD | Chicago | IL |
| 2050830.0 | 128.0 | SEA | ORD | Chicago | IL |
| 2171235.0 | 204.0 | SEA | ORD | Chicago | IL |
| 2170830.0 | 220.0 | SEA | ORD | Chicago | IL |
| 2031615.0 | 185.0 | SEA | ORD | Chicago | IL |
| 2171415.0 | 164.0 | SEA | ORD | Chicago | IL |
| 2261415.0 | 138.0 | SEA | ORD | Chicago | IL |
| 3120820.0 | 179.0 | SEA | ORD | Chicago | IL |
| 3121200.0 | 151.0 | SEA | ORD | Chicago | IL |
| 3200600.0 | 140.0 | SEA | ORD | Chicago | IL |
| 3051235.0 | 140.0 | SEA | ORD | Chicago | IL |
| 3120830.0 | 204.0 | SEA | ORD | Chicago | IL |
| 3132240.0 | 127.0 | SEA | ORD | Chicago | IL |
| 3162240.0 | 150.0 | SEA | ORD | Chicago | IL |
| 3181417.0 | 127.0 | SEA | ORD | Chicago | IL |
| 1091350.0 | 133.0 | SEA | MDW | Chicago | IL |
| 1261350.0 | 109.0 | SEA | MDW | Chicago | IL |
| 3171420.0 | 111.0 | SEA | MDW | Chicago | IL |
| 3310600.0 | 206.0 | SEA | MDW | Chicago | IL |
| 2271820.0 | 203.0 | SEA | COS | Colorado Springs | CO |
| 3171825.0 | 205.0 | SEA | COS | Colorado Springs | CO |
| 1250755.0 | 233.0 | SEA | JNU | Juneau | AK |
| 1310755.0 | 210.0 | SEA | JNU | Juneau | AK |
| 1311120.0 | 105.0 | SEA | JNU | Juneau | AK |
| 3030755.0 | 110.0 | SEA | JNU | Juneau | AK |
| 3130750.0 | 320.0 | SEA | JNU | Juneau | AK |
| 1062320.0 | 107.0 | SEA | DTW | Detroit | MI |
| 3121405.0 | 135.0 | SEA | ONT | Ontario | CA |
| 3130725.0 | 174.0 | SEA | ONT | Ontario | CA |
| 1171425.0 | 105.0 | SEA | LAX | Los Angeles | CA |
| 1041700.0 | 264.0 | SEA | LAX | Los Angeles | CA |
| 1051700.0 | 136.0 | SEA | LAX | Los Angeles | CA |
| 1071645.0 | 151.0 | SEA | LAX | Los Angeles | CA |
| 1311645.0 | 136.0 | SEA | LAX | Los Angeles | CA |
| 1251020.0 | 130.0 | SEA | LAX | Los Angeles | CA |
| 1261020.0 | 108.0 | SEA | LAX | Los Angeles | CA |
| 1291258.0 | 126.0 | SEA | LAX | Los Angeles | CA |
| 2022140.0 | 131.0 | SEA | LAX | Los Angeles | CA |
| 2130610.0 | 135.0 | SEA | LAX | Los Angeles | CA |
| 2091645.0 | 104.0 | SEA | LAX | Los Angeles | CA |
| 2131645.0 | 134.0 | SEA | LAX | Los Angeles | CA |
| 2031805.0 | 212.0 | SEA | LAX | Los Angeles | CA |
| 2071805.0 | 105.0 | SEA | LAX | Los Angeles | CA |
| 2071649.0 | 126.0 | SEA | LAX | Los Angeles | CA |
| 2081649.0 | 154.0 | SEA | LAX | Los Angeles | CA |
| 2091649.0 | 245.0 | SEA | LAX | Los Angeles | CA |
| 2131705.0 | 107.0 | SEA | LAX | Los Angeles | CA |
| 2161010.0 | 124.0 | SEA | LAX | Los Angeles | CA |
| 2271149.0 | 192.0 | SEA | LAX | Los Angeles | CA |
| 3141650.0 | 197.0 | SEA | LAX | Los Angeles | CA |
| 3152105.0 | 115.0 | SEA | LAX | Los Angeles | CA |
| 3171700.0 | 131.0 | SEA | LAX | Los Angeles | CA |
| 3291700.0 | 118.0 | SEA | LAX | Los Angeles | CA |
| 1050037.0 | 102.0 | SEA | MSP | Minneapolis | MN |
| 1070700.0 | 193.0 | SEA | MSP | Minneapolis | MN |
| 1130700.0 | 429.0 | SEA | MSP | Minneapolis | MN |
| 2240655.0 | 109.0 | SEA | MSP | Minneapolis | MN |
| 2121515.0 | 118.0 | SEA | MSP | Minneapolis | MN |
| 1060800.0 | 132.0 | SEA | MCO | Orlando | FL |
| 2220955.0 | 157.0 | SEA | SAN | San Diego | CA |
| 3010955.0 | 108.0 | SEA | SAN | San Diego | CA |
| 1031300.0 | 108.0 | SEA | ANC | Anchorage | AK |
| 1062110.0 | 149.0 | SEA | ANC | Anchorage | AK |
| 1132112.0 | 106.0 | SEA | ANC | Anchorage | AK |
| 2072350.0 | 141.0 | SEA | ANC | Anchorage | AK |
| 2091815.0 | 133.0 | SEA | ANC | Anchorage | AK |
| 3181605.0 | 187.0 | SEA | ANC | Anchorage | AK |
| 3231915.0 | 115.0 | SEA | ANC | Anchorage | AK |
| 3311605.0 | 135.0 | SEA | ANC | Anchorage | AK |
| 1022258.0 | 180.0 | SEA | JFK | New York | NY |
| 1042258.0 | 285.0 | SEA | JFK | New York | NY |
| 1022259.0 | 116.0 | SEA | JFK | New York | NY |
| 2090715.0 | 110.0 | SEA | JFK | New York | NY |
| 2022145.0 | 101.0 | SEA | JFK | New York | NY |
| 2032145.0 | 165.0 | SEA | JFK | New York | NY |
| 2031535.0 | 181.0 | SEA | JFK | New York | NY |
| 2042145.0 | 201.0 | SEA | JFK | New York | NY |
| 2142140.0 | 113.0 | SEA | JFK | New York | NY |
| 2222140.0 | 118.0 | SEA | JFK | New York | NY |
| 2032258.0 | 130.0 | SEA | JFK | New York | NY |
| 2220700.0 | 404.0 | SEA | JFK | New York | NY |
| 3310715.0 | 385.0 | SEA | JFK | New York | NY |
| 3192140.0 | 119.0 | SEA | JFK | New York | NY |
| 3091300.0 | 125.0 | SEA | JFK | New York | NY |
| 1241000.0 | 806.0 | SEA | OGG | Kahului, Maui | HI |
| 1030845.0 | 342.0 | SEA | PHL | Philadelphia | PA |
| 1050845.0 | 174.0 | SEA | PHL | Philadelphia | PA |
| 1210845.0 | 866.0 | SEA | PHL | Philadelphia | PA |
| 1022215.0 | 121.0 | SEA | PHL | Philadelphia | PA |
| 1031130.0 | 146.0 | SEA | PHL | Philadelphia | PA |
| 1090835.0 | 148.0 | SEA | PHL | Philadelphia | PA |
| 1170835.0 | 203.0 | SEA | PHL | Philadelphia | PA |
| 2030845.0 | 202.0 | SEA | PHL | Philadelphia | PA |
| 3130835.0 | 290.0 | SEA | PHL | Philadelphia | PA |
| 1031810.0 | 142.0 | SEA | SLC | Salt Lake City | UT |
| 1041016.0 | 117.0 | SEA | SLC | Salt Lake City | UT |
| 1291725.0 | 111.0 | SEA | SLC | Salt Lake City | UT |
| 1021555.0 | 175.0 | SEA | SLC | Salt Lake City | UT |
| 1041825.0 | 110.0 | SEA | SLC | Salt Lake City | UT |
| 2131635.0 | 134.0 | SEA | SLC | Salt Lake City | UT |
| 2230710.0 | 739.0 | SEA | SLC | Salt Lake City | UT |
| 2240710.0 | 477.0 | SEA | SLC | Salt Lake City | UT |
| 2061005.0 | 119.0 | SEA | SLC | Salt Lake City | UT |
| 3051315.0 | 123.0 | SEA | SLC | Salt Lake City | UT |
| 3260710.0 | 149.0 | SEA | SLC | Salt Lake City | UT |
| 3281750.0 | 125.0 | SEA | SLC | Salt Lake City | UT |
Vertex Degrees
inDegrees: Incoming connections to the airportoutDegrees: Outgoing connections from the airportdegrees: Total connections to and from the airport
Reviewing the various properties of the property graph to understand the incoming and outgoing connections between airports.
// Degrees
// The number of degrees - the number of incoming and outgoing connections - for various airports within this sample dataset
display(tripGraph.degrees.sort($"degree".desc).limit(20))
| id | degree |
|---|---|
| ATL | 179774.0 |
| DFW | 133966.0 |
| ORD | 125405.0 |
| LAX | 106853.0 |
| DEN | 103699.0 |
| IAH | 85685.0 |
| PHX | 79672.0 |
| SFO | 77635.0 |
| LAS | 66101.0 |
| CLT | 56103.0 |
| EWR | 54407.0 |
| MCO | 54300.0 |
| LGA | 50927.0 |
| SLC | 50780.0 |
| BOS | 49936.0 |
| DTW | 46705.0 |
| MSP | 46235.0 |
| SEA | 45816.0 |
| JFK | 43661.0 |
| BWI | 42526.0 |
City / Flight Relationships through Motif Finding
To more easily understand the complex relationship of city airports and their flights with each other, we can use motifs to find patterns of airports (i.e. vertices) connected by flights (i.e. edges). The result is a DataFrame in which the column names are given by the motif keys.
/*
Using tripGraphPrime to more easily display
- The associated edge (ab, bc) relationships
- With the different the city / airports (a, b, c) where SFO is the connecting city (b)
- Ensuring that flight ab (i.e. the flight to SFO) occured before flight bc (i.e. flight leaving SFO)
- Note, TripID was generated based on time in the format of MMDDHHMM converted to int
- Therefore bc.tripid < ab.tripid + 10000 means the second flight (bc) occured within approx a day of the first flight (ab)
Note: In reality, we would need to be more careful to link trips ab and bc.
*/
val motifs = tripGraphPrime.
find("(a)-[ab]->(b); (b)-[bc]->(c)").
filter("(b.id = 'SFO') and (ab.delay > 500 or bc.delay > 500) and bc.tripid > ab.tripid and bc.tripid < ab.tripid + 10000")
display(motifs)
Determining Airport Ranking using PageRank
There are a large number of flights and connections through these various airports included in this Departure Delay Dataset. Using the pageRank algorithm, Spark iteratively traverses the graph and determines a rough estimate of how important the airport is.
// Determining Airport ranking of importance using `pageRank`
val ranks = tripGraph.pageRank.resetProbability(0.15).maxIter(5).run()
ranks: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 3 more fields], e:[src: string, dst: string ... 5 more fields])
display(ranks.vertices.orderBy($"pagerank".desc).limit(20))
| id | City | State | Country | pagerank |
|---|---|---|---|---|
| ATL | Atlanta | GA | USA | 18.910104616729814 |
| DFW | Dallas | TX | USA | 13.699227467378964 |
| ORD | Chicago | IL | USA | 13.163049993795985 |
| DEN | Denver | CO | USA | 9.723388283811563 |
| LAX | Los Angeles | CA | USA | 8.703656827807166 |
| IAH | Houston | TX | USA | 7.991324463091128 |
| SFO | San Francisco | CA | USA | 6.903242998287933 |
| PHX | Phoenix | AZ | USA | 6.505886984498643 |
| SLC | Salt Lake City | UT | USA | 5.799587684561128 |
| LAS | Las Vegas | NV | USA | 5.25359244560915 |
| SEA | Seattle | WA | USA | 4.626877547905697 |
| EWR | Newark | NJ | USA | 4.401221169028188 |
| MCO | Orlando | FL | USA | 4.389045874474043 |
| CLT | Charlotte | NC | USA | 4.378459524081744 |
| DTW | Detroit | MI | USA | 4.223377976049847 |
| MSP | Minneapolis | MN | USA | 4.1490048912541795 |
| LGA | New York | NY | USA | 4.129454491295321 |
| BOS | Boston | MA | USA | 3.812077076528526 |
| BWI | Baltimore | MD | USA | 3.53116352570383 |
| JFK | New York | NY | USA | 3.521942669296845 |
BTW, A lot more delicate air-traffic arithmetic is possible for a full month of airplane co-trajectories over the radar range of Atlanta, Georgia, one of the busiest airports in the world.
See for instance:
- Statistical regular pavings to analyze massive data of aircraft trajectories, Gloria Teng, Kenneth Kuhn and Raazesh Sainudiin, Journal of Aerospace Computing, Information, and Communication, Vol. 9, No. 1, pp. 14-25, doi: 10.2514/1.I010015, 2012. See free preprint: http://lamastex.org/preprints/AAIASubPavingATC.pdf.
Most popular flights (single city hops)
Using the tripGraph, we can quickly determine what are the most popular single city hop flights
// Determine the most popular flights (single city hops)
import org.apache.spark.sql.functions._
val topTrips = tripGraph.edges.
groupBy("src", "dst").
agg(count("delay").as("trips"))
import org.apache.spark.sql.functions._
topTrips: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
// Show the top 20 most popular flights (single city hops)
display(topTrips.orderBy($"trips".desc).limit(20))
| src | dst | trips |
|---|---|---|
| SFO | LAX | 3232.0 |
| LAX | SFO | 3198.0 |
| LAS | LAX | 3016.0 |
| LAX | LAS | 2964.0 |
| JFK | LAX | 2720.0 |
| LAX | JFK | 2719.0 |
| ATL | LGA | 2501.0 |
| LGA | ATL | 2500.0 |
| LAX | PHX | 2394.0 |
| PHX | LAX | 2387.0 |
| HNL | OGG | 2380.0 |
| OGG | HNL | 2379.0 |
| LAX | SAN | 2215.0 |
| SAN | LAX | 2214.0 |
| SJC | LAX | 2208.0 |
| LAX | SJC | 2201.0 |
| ATL | MCO | 2136.0 |
| MCO | ATL | 2090.0 |
| JFK | SFO | 2084.0 |
| SFO | JFK | 2084.0 |
Top Transfer Cities
Many airports are used as transfer points instead of the final Destination. An easy way to calculate this is by calculating the ratio of inDegree (the number of flights to the airport) / outDegree (the number of flights leaving the airport). Values close to 1 may indicate many transfers, whereas values < 1 indicate many outgoing flights and > 1 indicate many incoming flights. Note, this is a simple calculation that does not take into account of timing or scheduling of flights, just the overall aggregate number within the dataset.
// Calculate the inDeg (flights into the airport) and outDeg (flights leaving the airport)
val inDeg = tripGraph.inDegrees
val outDeg = tripGraph.outDegrees
// Calculate the degreeRatio (inDeg/outDeg), perform inner join on "id" column
val degreeRatio = inDeg.join(outDeg, inDeg("id") === outDeg("id")).
drop(outDeg("id")).
selectExpr("id", "double(inDegree)/double(outDegree) as degreeRatio").
cache()
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val nonTransferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA").
selectExpr("id", "city", "degreeRatio").
filter("degreeRatio < 0.9 or degreeRatio > 1.1")
// List out the city airports which have abnormal degree ratios
display(nonTransferAirports)
| id | city | degreeRatio |
|---|---|---|
| GFK | Grand Forks | 1.3333333333333333 |
| FAI | Fairbanks | 1.1232686980609419 |
| OME | Nome | 0.5084745762711864 |
| BRW | Barrow | 0.28651685393258425 |
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val transferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA"). //degreeRatio.join(airports, degreeRatio("id") === airports("IATA")).
selectExpr("id", "city", "degreeRatio").
filter("degreeRatio between 0.9 and 1.1")
// List out the top 10 transfer city airports
display(transferAirports.orderBy("degreeRatio").limit(10))
| id | city | degreeRatio |
|---|---|---|
| MSP | Minneapolis | 0.9375183338222353 |
| DEN | Denver | 0.958025717037065 |
| DFW | Dallas | 0.964339653074092 |
| ORD | Chicago | 0.9671063983310065 |
| SLC | Salt Lake City | 0.9827417906368358 |
| IAH | Houston | 0.9846895050147083 |
| PHX | Phoenix | 0.9891643572266746 |
| OGG | Kahului, Maui | 0.9898718478710211 |
| HNL | Honolulu, Oahu | 0.990535889872173 |
| SFO | San Francisco | 0.9909473252295224 |
Breadth First Search
Breadth-first search (BFS) is designed to traverse the graph to quickly find the desired vertices (i.e. airports) and edges (i.e flights). Let's try to find the shortest number of connections between cities based on the dataset. Note, these examples do not take into account of time or distance, just hops between cities.
// Example 1: Direct Seattle to San Francisco
// This method returns a DataFrame of valid shortest paths from vertices matching "fromExpr" to vertices matching "toExpr"
val filteredPaths = tripGraph.bfs.fromExpr((col("id") === "SEA")).toExpr(col("id") === "SFO").maxPathLength(1).run()
display(filteredPaths)
As you can see, there are a number of direct flights between Seattle and San Francisco.
// Example 2: Direct San Francisco and Buffalo
// You can also specify expression as a String, instead of Column
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(1).run()
filteredPaths: org.apache.spark.sql.DataFrame = [id: string, City: string ... 2 more fields]
filteredPaths.show()
+---+----+-----+-------+
| id|City|State|Country|
+---+----+-----+-------+
+---+----+-----+-------+
display(filteredPaths) // display instead of show - same diference
But there are no direct flights between San Francisco and Buffalo.
// Example 2a: Flying from San Francisco to Buffalo
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(2).run()
display(filteredPaths)
But there are flights from San Francisco to Buffalo with Minneapolis as the transfer point.
Loading the D3 Visualization
Using the airports D3 visualization to visualize airports and flight paths
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3a1.graphs.help()
Produces a force-directed graph given a collection of edges of the following form: case class Edge(src: String, dest: String, count: Long)
Usage:
%scala
import d3._
graphs.force(
height = 500,
width = 500,
clicks: Dataset[Edge])
// On-time and Early Arrivals
import d3a1._
graphs.force(
height = 800,
width = 1200,
clicks = sql("select src, dst as dest, count(1) as count from departureDelays_geo where delay <= 0 group by src, dst").as[Edge])

